2023· International Journal of Data Engineering and Intelligent Computing· Vol 6, pp. 01-13· 0 citations
TL;DR
An ontology-guided approach to Data Mesh design is proposed, leveraging domain ontologies to ensure semantic interoperability, governance automation, and federated data product discovery and demonstrates how ontologies facilitate coherent data governance across distributed teams while preserving the autonomy of individual domains.
Abstract
The rise of Data Mesh as a paradigm for large-scale data management has challenged the traditional monolithic data lake approach by promoting decentralized ownership, domain-oriented architecture, and self-serve infrastructure. However, without a unified semantic layer, decentralization can lead to fragmentation, inconsistency, and governance bottlenecks. This paper proposes an ontology-guided approach to Data Mesh design, leveraging domain ontologies to ensure semantic interoperability, governance automation, and federated data product discovery. By aligning domain-specific knowledge structures with data product metadata, we demonstrate how ontologies facilitate coherent data governance across distributed teams while preserving the autonomy of individual domains. We explore architectural patterns, governance workflows, and implementation considerations, and present a case study to illustrate the application of this approach in a real-world enterprise setting.
A metadata-centric approach to managing the semantic layer in multi-domain data pipelines that leverages active metadata, semantic annotations, knowledge graphs, and policy-driven governance to enable scalable, federated analytics is proposed.
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The paper explores architectural patterns, implementation considerations, and case studies demonstrating the benefits of this metadata-driven approach to data transformation within Data Mesh architectures in enhancing interoperability, governance, and agility in enterprise data systems.
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