The differences between data integration architecture concepts

Posted by Sam Chance on Jun 22, 2021 7:15:00 AM
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This blog is the first post of a series titled An Integrated Data Enterprise.

A portion of this blog is an excerpt from the O’Reilly ebook The Rise of the Knowledge Graph co-authored by Ben Szekely, Dean Allemang, and Sean Martin. If you are curious to learn more about knowledge graph and how it stitches together the concepts in this post, please check out the ebook.

Like many advanced technologies, the knowledge graph can be used in many ways. It is quite possible to use it to build a single, standalone application; in fact, many enterprises have done just that to build applications that provide flexible and insightful analyses. But powerful as such applications are, they are still just another application in a field of silos, not connected to any other data or applications.


In recent years, there has been a growing awareness that enterprise data continues to be distributed in nature, and that conventional, centralized views of data management are insufficient for addressing the growing needs businesses place on data management and access. A knowledge graph is, as part of its very design, a distributed system. And as such, it is an excellent choice for supporting an enterprise data practice that acknowledges the distributed nature of enterprise data. This involves using the knowledge graph well beyond the scope of a single application, no matter how insightful.

A variety of concepts have been developed about how to manage data that is distributed. None of these really constitutes a “solution” as it were; they are developments in thinking about data management, all of which are responding to the same issues we talk about in The Rise of the Knowledge Graph. As such, the boundaries between them are vague at best. Here are approaches and movements that represent cutting-edge thinking about this topic:

Data fabric

Discussed in detail in The Rise of the Knowledge Graph, a data fabric is a design concept for enterprise data architecture, emphasizing flexibility, extensibility, accessibility, and connecting data at scale. Aside from the ebook, learn more about data fabrics here or grab this Gartner report discussing data fabric design.

Data mesh

This is an architecture for managing data as a distributed network of self-describing products in the enterprise, where data provisioning is taken as seriously as any other product in the enterprise.

Data-centric revolution

Some data management analysts see the issues with enterprise data management we have described here, and conclude that there must be a significant change in how we think about enterprise data. The change is so significant that they refer to it as a revolution in data management. The fundamental observation of the data-centric revolution is that business applications come and go, but the data of a business retains its value indefinitely. Emphasizing the role of durable data in an enterprise is a fundamental change in how we view enterprise data architecture.

FAIR data

Going beyond just the enterprise, the FAIR data movement (findable, accessible, interoperable, reusable data) outlines practices for data sharing on a global scale that encourages an interoperable data landscape.


All of these approaches strive to create an environment where data is shared and managed as a valuable resource in its own right, and where it becomes more valuable as a shared resource than as a private resource.

None of these approaches or movements specifies a technological solution. But we strongly believe that the best way to achieve the goals of each of these movements is through appropriate use of a knowledge graph. The knowledge graph must not simply become yet another warehousing application in the enterprise, adding just one more application to the quagmire of data applications. Instead, the knowledge graph has to become the backbone of a distributed data strategy, enabling the interoperability of data sources across the enterprise.

If looking to progress your understand of knowledge graphs or data fabrics, the ebook The Rise of the Knowledge Graph is a great place to start. You can also find information on how knowledge graphs are being applied to industries like Life Sciences, Manufacturing, Financial Services, and Government on the

This blog is the first post of the series An Integrated Data Enterprise. Keep an out for the next chapters:

Tags: Data Integration, Data Fabric

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