From Data Lakes to Data Fabric/Mesh: The Future of Enterprise Data Platforms in a Multi-Cloud World

The evolution of enterprise data management has seen a shift from traditional data warehouses to data lakes, and now towards data fabrics and data meshes. This article explores this progression, particularly in the context of multi-cloud environments. Data fabrics provide a unified, virtualized view of data across disparate sources, while data meshes decentralize data ownership and governance, aligning with modern, agile development practices. The article discusses how these architectures can be implemented in multi-cloud setups, ensuring data consistency, security, and performance. It also addresses the challenges and opportunities presented by this shift, such as integrating AI and ML workloads into the data platform. The article concludes with a forward-looking perspective on how enterprise data platforms will continue to evolve to meet the demands of a multi-cloud, data-driven world.

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Publication Details

Journal
Journal of Computer Science and Technology Studies
Published
2025-05-29
DOI
https://doi.org/10.32996/jcsts.2025.7.5.4
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
0.00
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article

From Data Lakes to Data Fabric/Mesh: The Future of Enterprise Data Platforms in a Multi-Cloud World

Journal of Computer Science and Technology Studies
Big Data and Business Intelligence
article

From Data Lakes to Data Fabric/Mesh: The Future of Enterprise Data Platforms in a Multi-Cloud World

article en

Abstract

The evolution of enterprise data management has seen a shift from traditional data warehouses to data lakes, and now towards data fabrics and data meshes. This article explores this progression, particularly in the context of multi-cloud environments. Data fabrics provide a unified, virtualized view of data across disparate sources, while data meshes decentralize data ownership and governance, aligning with modern, agile development practices. The article discusses how these architectures can be implemented in multi-cloud setups, ensuring data consistency, security, and performance. It also addresses the challenges and opportunities presented by this shift, such as integrating AI and ML workloads into the data platform. The article concludes with a forward-looking perspective on how enterprise data platforms will continue to evolve to meet the demands of a multi-cloud, data-driven world.

Journal of Computer Science and Technology StudiesVol. 7(5)
REL (United States) (US)
Openalex Percentile: Top 80%
Big Data and Business Intelligence
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