The Model-Driven Enterprise Data Fabric: A Proposal Based on Conceptual Modelling and Knowledge Graphs

Ghiran, Ana-Maria; Buchmann, Robert Andrei

doi:10.1007/978-3-030-29551-6_51

Cited by 7 publications

(4 citation statements)

References 13 publications

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“…Outside of the realm of orchestration frameworks, novel metadata management concepts such as data mesh [70] and data fabric [71], [72] have recently emerged to mitigate the problems of inflexibility to changing requirements and scalability of traditional storage solutions such as data warehouses and data lakes. They operate in a distributed manner, processing data locally and publishing it to centralized metadata catalogues, and can provide critical support for heterogeneous data from distributed environments and across clusters and different organizations.…”

Section: E Metadata Managementmentioning

confidence: 99%

A Framework for Cognitive, Decentralized Container Orchestration

Sofia,

Salomon,

Ferlin-Reiter

et al. 2024

IEEE Access

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This perspective paper introduces a novel framework for container orchestration called CODECO. The CODECO orchestration framework relies on a data-network-computing approach to define the best infrastructure that can support the operation of next-generation Internet applications across a mobile, heterogeneous Edge-Cloud continuum. The selection of such an infrastructure is aligned with target performance profiles defined by the user, such as resilience or greenness. CODECO proposes to rely on decentralized Artificial Intelligence approaches in an attempt to provide the most suitable infrastructure to an application deployment, considering infrastructural challenges, such as intermittent connectivity and node failure. This paper explains the current CODECO framework and provides insight into operational use-cases where CODECO is being deployed, as relevant examples of application for such a framework. Recent developments in the creation of the open-source CODECO framework are described and explained, allowing the use of the framework by the research community. The paper then provides a thorough analysis of CODECO's features in comparison with existing orchestration frameworks, explaining the benefits introduced with this dynamic orchestration approach.INDEX TERMS Edge-Cloud; Orchestration; Kubernetes; AI/ML; heterogeneous networks; data observability.

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Section: E Metadata Managementmentioning

confidence: 99%

A Framework for Cognitive, Decentralized Container Orchestration

Sofia,

Salomon,

Ferlin-Reiter

et al. 2024

IEEE Access

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“…A related but different term is data fabric. Data fabric is a technical architecture that brings together heterogeneous data that spans across multiple data sources; it allows organizations to monitor and manage data regardless of the location, considering appropriate data governance and data cataloging [11]. To provide access for users throughout the organization, data fabric uses rich metadata and a data virtualization layer [17].…”

Section: Gpmentioning

confidence: 99%

“…Definition 5: Data fabric. The data fabric architecture bridges the data-knowledge-gap using knowledge-based integration techniques to bring together heterogeneous data, that spans across multiple sources or formats, considering appropriate data governance and data cataloging [11].…”

Section: Gpmentioning

confidence: 99%

Data Mesh: Motivational Factors, Challenges, and Best Practices

Bode¹,

Kühl²,

Kreuzberger³

et al. 2023

Preprint

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With the increasing importance of data and artificial intelligence, organizations strive to become more data-driven. However, current data architectures are not necessarily designed to keep up with the scale and scope of data and analytics use cases. In fact, existing architectures often fail to deliver the promised value associated with them. Data mesh is a socio-technical concept that includes architectural aspects to promote data democratization and enables organizations to become truly data-driven. As the concept of data mesh is still novel, it lacks empirical insights from the field. Specifically, an understanding of the motivational factors for introducing data mesh, the associated challenges, best practices, its business impact, and potential archetypes, is missing. To address this gap, we conduct 15 semi-structured interviews with industry experts. Our results show, among other insights, that industry experts have difficulties with the transition toward federated governance associated with the data mesh concept, the shift of responsibility for the development, provision, and maintenance of data products, and the concept of a data product model. In our work, we derive multiple best practices and suggest organizations embrace elements of data fabric, observe the data product usage, create quick wins in the early phases, and favor small dedicated teams that prioritize data products. While we acknowledge that organizations need to apply best practices according to their individual needs, we also deduct two archetypes that provide suggestions in more detail. Our findings synthesize insights from industry experts and provide researchers and professionals with guidelines for the successful adoption of data mesh.

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“…Data fabric can be deemed as the technology part of data mesh. It emphasizes data virtualization, metadata management, and API-based integration to create a unified data fabric that stakeholders can access and consume [104].…”

Section: ) Data Fabric Architecturesmentioning

confidence: 99%