<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>Modern organizations are currently wrestling with strenuous challenges relating to the management of heterogeneous big data, which combines data from various sources and varies in type, format, and content. The heterogeneity of the data makes it difficult to analyze and integrate. This paper presents big data warehousing and federation as viable approaches for handling big data complexity. It discusses their respective advantages and disadvantages as strategies for integrating, managing, and analyzing heterogeneous big data. Data integration is crucial for organizations to manipulate organizational data. Organizations have to weigh the benefits and drawbacks of both data integration approaches to identify the one that responds to their organizational needs and objectives. This paper aw well presents an adequate analysis of these two data integration approaches and identifies challenges associated with the selection of either approach. Thorough understanding and awareness of the merits and demits of these two approaches are crucial for practitioners, researchers, and decision-makers to select the approach that enables them to handle complex data, boost their decision-making process, and best align with their needs and expectations.</p>
</abstract>

Modern organizations are currently wrestling with strenuous challenges relating to the management of heterogeneous big data, which combines data from various sources and varies in type, format, and content. The heterogeneity of the data makes it difficult to analyze and integrate. This paper presents big data warehousing and federation as viable approaches for handling big data complexity. It discusses their respective advantages and disadvantages as strategies for integrating, managing, and analyzing heterogeneous big data. Data integration is crucial for organizations to manipulate organizational data. Organizations have to weigh the benefits and drawbacks of both data integration approaches to identify the one that responds to their organizational needs and objectives. This paper aw well presents an adequate analysis of these two data integration approaches and identifies challenges associated with the selection of either approach. Thorough understanding and awareness of the merits and demits of these two approaches are crucial for practitioners, researchers, and decision-makers to select the approach that enables them to handle complex data, boost their decision-making process, and best align with their needs and expectations.

Integration Approaches for Heterogeneous Big Data: A Survey

Department of Intelligence Systems, Faculty of Artificial Intelligence

Department of Basic Sciences, Faculty of Science

Institute of Computer Science and Digital Innovation

Faculty of Computer Science and Engineering

Department of Computer Information Systems

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Integration Approaches for Heterogeneous Big Data: A Survey"}

Modern organizations are currently wrestling with strenuous challenges relating to the management of heterogeneous big data, which combines data from various...

Integration Approaches for Heterogeneous Big Data: A Survey

Published Online: Mar 23, 2024

Page range: 3 - 20

Received: Oct 02, 2023

Accepted: Feb 26, 2024

DOI: https://doi.org/10.2478/cait-2024-0001

Keywords
Heterogeneous Big Data, Integration approaches, Big Data warehousing, Big Data federation

© 2024 Wafa’ Za’al Alma’aitah et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Integration Approaches for Heterogeneous Big Data: A Survey

Published Online: Mar 23, 2024

Page range: 3 - 20

Received: Oct 02, 2023

Accepted: Feb 26, 2024

DOI: https://doi.org/10.2478/cait-2024-0001

KeywordsHeterogeneous Big Data, Integration approaches, Big Data warehousing, Big Data federation

© 2024 Wafa’ Za’al Alma’aitah et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
Heterogeneous Big Data, Integration approaches, Big Data warehousing, Big Data federation