International Journal of Computational and Electronic Aspects in Engineering
Volume 7 · Issue 3 · July 2026 · pp. 54-62
Special Issue of National Conference on Emerging Innovative Trends in Computer Applications
Review Article · Peer Reviewed
Received: June 10, 2026 · Accepted: July 20, 2026 · Published: 31 July 2026
DOI: 10.26706/ijceae.7.4.20260708
Open Access · CC BY 4.0

An Integrated Study of Data Warehousing Techniques

Dr. Madhura Naralkar1*, Vishal Bhonde2, Ekta Maske3, Divya Bhagat4
1,2,3,4 MCA, Suryodaya College of Engineering and Technology, Nagpur, India.

*Corresponding author: madhura.naralkar@gmail.com
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Abstract

In today’s digital environment, organizations continuously produce large volumes of data from multiple sources, including transaction processing systems, social media, sensors and various online applications. Effectively handling and analyzing this ever-growing data has become a major concern for decision makers. Data warehousing helps overcome this challenge by offering a centralized platform where data from different sources are collected, integrated and stored over time in a consistent manner for analysis and reporting. This research paper provides an integrated overview of data warehousing techniques, covering key aspects such as architecture, data modeling methods, ETL processes, storage mechanisms and emerging developments in the field. This study aims to present a clear and easy-to-understand explanation of how data warehousing supports business intelligence and informed strategic decision-making. Additionally, this paper highlights existing challenges and explores future possibilities, making it valuable for students, researchers and industry professionals.[1]

Keywords

Data Warehouse ETL Data Warehousing Techniques OLAP Business Intelligence

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