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Data Fabric Architecture Patterns

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A specific Data Fabric architecture is determined by its business and IT context and intent, meaning that not every implementation is identical. A Data Fabric could for instance serve different data consumption patterns, such as real-time transactional inference of AI-based insights, trustworthy AI scenarios, or AI governance purposes. A specific implementation of a Data Fabric also depends on concrete solution requirements, such as the ones associated with a Data Mesh solution (e.g., data-as-a-product) and whether the Data Fabric should serve certain technologies, such as IoT, edge computing, or 5G. Finally, intelligent information integration can be underpinned with different and complementary methods, such as data virtualization, replication, streaming, etc., which has an impact on the underlying Data Fabric architecture. Integration challenges within a hybrid cloud landscape leveraging public cloud services may differ from integration needs within a private cloud and on-premises landscape.

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  1. 1.

    See Reference [1] for more information on Data Fabric in a hybrid cloud environment.

  2. 2.

    See Chapter 6 for some sample AI application areas in various industries.

  3. 3.

    Refer to Chapter 2, where we have introduced the origin of the Data Fabric term.

  4. 4.

    See References [2] and [3] for more information on the relationship between the Data Fabric, IoT, and edge computing.

  5. 5.

    We explore this aspect in the following section, “Data Consumption Patterns.”

  6. 6.

    We introduce a Data Fabric architecture for a Data Mesh solution later in this chapter.

  7. 7.

    See Chapter 9.

  8. 8.

    See the section “Trustworthy AI” in Chapter 5.

  9. 9.

    See Reference [4] for more information on data consumption patterns.

  10. 10.

    MDM stands for Master Data Management.

  11. 11.

    See Reference [5] for more information on MDM solutions and deployment options.

  12. 12.

    See Reference [6] for more details on self-service data platforms.

  13. 13.

    JSON stands for JavaScript Object Notation and is a lightweight data interchange format. See for more details.

  14. 14.

    Apache Avro is a data serialization framework. See for more details.

  15. 15.

    See Reference [7] for more on information integration and [8] for a recent magic quadrant from Gartner on data integration tools.

  16. 16.

    CDC stands for change data capture.

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© 2023 The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature

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Hechler, E., Weihrauch, M., Wu, Y.(. (2023). Data Fabric Architecture Patterns. In: Data Fabric and Data Mesh Approaches with AI. Apress, Berkeley, CA.

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