Data Integration in Heterogeneous Systems

Abstract

This is an open access article under the CCBY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/2.0/) Abstract: Integrating data from heterogeneous systems is a critical challenge in modern data management. The increasing diversity of data sources such as relational databases, NoSQL databases, cloud storage, and legacy systems complicates the process of unifying data for analytics, decision-making, and machine learning. This paper reviews key challenges in heterogeneous data integration and explores traditional and modern integration techniques, including ETL, data federation, and data virtualization. We also provide a comparative analysis of these approaches and propose potential solutions to address scalability, real-time access, and schema integration. Case studies and performance evaluation are presented, highlighting real-world applications in healthcare and finance.