31, March 2025

AI-Driven Data Warehousing in Real-Time Business Intelligence: A Framework for Automated ETL, Predictive Analytics, and Cloud Integration

VOLUME-9; ISSUE-3; MAR-2025

Author(s): 1. Akash Vijayrao Chaudhari, 2. Pallavi Ashokrao Charate

Authors Affiliations:

  1. Senior Associate, Santander Bank, Florham Park, NJ, USA
  2. Senior Systems Analyst, Worldpay, Cincinnati, OH, USA

DOIs:10.2017/IJRCS/202503025     |     Paper ID: IJRCS202503025


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Traditional data warehousing and business intelligence (BI) solutions face challenges in handling real-time data processing, manual ETL processes, and predictive analytics. With the rise of AI, there is an increasing need to optimize data warehousing processes for real-time decision-making. This research proposes an AI-driven framework that automates ETL, integrates real-time predictive analytics, and leverages cloud-based architectures to improve business intelligence efficiency. The methodology includes developing machine learning models for automated data ingestion, real-time trend analysis, and scalability using serverless computing. Our experiments on real-world datasets demonstrate significant improvements in ETL speed, prediction accuracy, and cloud cost optimization. The findings contribute to the advancement of AI-powered data warehousing and have strong implications for both academia and industry.

AI, Data Warehousing, Real-Time BI, ETL, Predictive Analytics, Cloud Computing

1. Akash Vijayrao Chaudhari, 2. Pallavi Ashokrao Charate(2025);  AI-Driven Data Warehousing in Real-Time Business Intelligence: A Framework for Automated ETL, Predictive Analytics, and Cloud Integration, International Journal of Research Culture Society,    ISSN(O): 2456-6683,  Volume – 9,   Issue –  3.,  Pp.185-189.       Available on – https://ijrcs.org/

Academic Journals:

  1. Chaudhari, A. V., & Charate, P. A. (2024). Data Warehousing for IoT Analytics. International Research Journal of Engineering and Technology (IRJET), 11(6), 311–320.
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Industry Reports:

  1. Gartner. (2022). Market Guide for Data Warehousing Solutions.
  2. Forrester. (2021). The Future of Data Warehousing and Analytics in the Cloud.
  3. McKinsey & Company. (2022). Harnessing AI and Data Science for Business Growth.
  4. IDC. (2023). The Role of Artificial Intelligence in Cloud Data Warehousing.
  5. Deloitte. (2023). Emerging Trends in AI-Powered Business Intelligence.

 

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