Data Warehousing and Its Impact on Machine Learning Model Efficiency: Comparing Snowflake, BigQuery, and Redshift

@article{1707511,
      author = {Bhanu Prakash Reddy Rella},
      title = {Data Warehousing and Its Impact on Machine Learning Model Efficiency: Comparing Snowflake, BigQuery, and Redshift},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {10},
      pages = {824-833},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707511.pdf},
      abstract = {Data warehousing plays a crucial role in optimizing machine learning (ML) model efficiency by enabling seamless data storage, retrieval, and processing. With the growing demand for scalable and high-performance ML applications, cloud-based data warehouses such as Snowflake, Google BigQuery, and Amazon Redshift have emerged as leading solutions. This study compares these platforms based on key performance metrics, including query execution speed, scalability, cost efficiency, and integration with ML workflows. Snowflake offers dynamic scalability and automated performance tuning, while BigQuery excels in serverless architecture and real-time analytics. Redshift, optimized for structured data, provides cost-effective performance for large-scale ML workloads. The findings highlight how selecting the right data warehousing solution can significantly impact ML model training times, accuracy, and overall efficiency.},
      keywords = {Data Warehousing, Machine Learning, Snowflake, BigQuery, Redshift, Model Efficiency, Cloud Computing, Performance Optimization.},
      month = {April},
  }