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Data Warehouse Automation: Streamlining Multi-Cloud ETL Workflows for Real-Time Analytics

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  • Lakshmi Ayyappan

Abstract

This article examines the transformative impact of automation technologies on data warehouse management and multi-cloud ETL workflows in enterprise environments. The article explores how organizations leverage advanced automation solutions to address the growing complexity of real-time analytics and data processing requirements. Through comprehensive article analysis of implementation strategies, the article demonstrates how modern data warehouse automation incorporates artificial intelligence, machine learning, and sophisticated orchestration mechanisms to enhance operational efficiency and data quality. The article shows the evolution from traditional ETL to modern ELT approaches, examining how this shift has revolutionized data processing capabilities while reducing development complexity. Key findings highlight the significant improvements in processing speed, resource utilization, and cost efficiency achieved through automated workflows. The article also addresses aspects of critical security, governance, and compliance automation, demonstrating how organizations can maintain robust control frameworks while scaling their data operations. Examining real-world implementations and industry best practices, this study provides valuable insights into the future direction of data warehouse automation and its role in enabling digital transformation.

Suggested Citation

  • Lakshmi Ayyappan, 2025. "Data Warehouse Automation: Streamlining Multi-Cloud ETL Workflows for Real-Time Analytics," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(1), pages 1534-1543, February.
    • Handle: RePEc:jbh:ijsrcs:v11:y2025:i1:id:820
    DOI: 10.32628/CSEIT251112166
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT251112166
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