Energy-Efficient Machine Learning in Cloud Data Warehousing: A Comparative Study on Snowflake and AWS Redshift

  • Authors

    • Sanjay Verma Operations Manager, HCL Technologies, India. Author
    • Dr. Nandhini Ravi Assistant Professor, VIT University, India. Author

    DOI:

    https://doi.org/10.67228/30715717/IJDEIC-2020PII6F2X

    Published 11-13-2020

  • Energy efficiency, Machine learning, Cloud data warehousing, Snowflake, AWS Redshift, Sustainability, Cloud computing, Performance benchmarking, Cost-efficiency, Green computing

    Issue

    Vol. 3 No. 2 (2020)

    Section

    Articles

    How to Cite

    [1]
    S. Verma and N. Ravi, “Energy-Efficient Machine Learning in Cloud Data Warehousing: A Comparative Study on Snowflake and AWS Redshift”, IJDEIC, vol. 3, no. 2, pp. 01–07, Nov. 2020, doi: 10.67228/30715717/IJDEIC-2020PII6F2X.

  • Abstract

    Cloud data warehousing platforms have become pivotal in enabling large-scale machine learning (ML) workloads, but their energy consumption poses significant sustainability challenges. This paper presents a comparative study of two leading cloud data warehousing solutions—Snowflake and AWS Redshift—focusing on their energy efficiency in executing machine learning tasks. By benchmarking a range of ML workloads on both platforms, we evaluate performance, energy consumption, and cost-efficiency. Our findings highlight critical trade-offs and reveal that while both platforms offer robust ML support, differences in architecture and resource management significantly impact their energy profiles. The study provides actionable insights for optimizing ML workloads in cloud data warehouses, contributing to greener and more cost-effective cloud computing practices.

  • References

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