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Data Fabric Deep Dive Part 4: What’s in a Name?

In our last blog in this series, we took a deeper dive into the components that make up a Data Fabric and Data Mesh. If you look closely, the first thing that may have jumped out is that there’s significant overlap between the two lists. Here’s a quick recap. Both a Data Fabric and a Data Mesh require the following:


  • Data Discoverability

  • Data Access

  • Self-Service

  • Data Governance

  • Security


The Data Fabric and Data Mesh: complementary approaches


So, as we alluded to in the second part of this blog series, maybe a Data Fabric is NOT a competing approach to a Data Mesh. Or rather, it does NOT have to be. What if a Data Fabric is actually complementary to a Data Mesh? What if having a Data Fabric implemented first accelerates the implementation of a Data Mesh, should it be needed later on?


In this blog, we’ll discuss how a Data Fabric actually supports building a Data Mesh because the Fabric provides the underlying data management and integration framework that enables the Data Mesh's core principles to function effectively.


To recap what we discussed in Part 2 of this series, here's how a Data Fabric contributes to building a Data Mesh:

  • Decentralized data management: A Data Fabric enables seamless access, integration, and management of data across various sources and domains. This supports the Data Mesh's decentralized approach by allowing domain teams to work independently while still having access to data from other domains as needed.

  • Domain-oriented architecture: A Data Fabric's ability to provide a unified and consistent view of data across disparate sources supports the Data Mesh's domain-oriented architecture. It allows each domain team to focus on their specific data products while still having the ability to access and share data with other teams.

  • Self-serve data platform: A Data Fabric simplifies data access, integration, and management by abstracting the underlying complexities. This makes it easier for domain teams to access and work with data, promoting the self-serve data platform capabilities emphasized in the Data Mesh paradigm.

  • Data discoverability: A Data Fabric can help improve data discoverability through metadata management, data cataloging, and data lineage capabilities. These features are essential for a Data Mesh, as they enable domain teams to find, understand, and use data from other domains effectively.

  • Data governance and compliance: A Data Fabric provides a foundation for maintaining data governance and compliance within a Data Mesh. It supports data quality, security, and privacy, ensuring that data usage adheres to organizational policies and regulatory requirements.

  • Scalability and flexibility: A Data Fabric's scalable and flexible nature allows it to grow with the organization, supporting the evolving needs of a Data Mesh. It can accommodate new data sources, formats, and integration requirements, ensuring the data infrastructure remains agile and responsive.


In short, a Data Fabric is highly relevant to building a Data Mesh, as it provides the necessary data management and integration capabilities to support the Data Mesh's core principles. Leveraging a Data Fabric enables organizations to create and deploy a robust, scalable, highly efficient data infrastructure, enabling domain teams to work independently while still benefiting from shared data resources and insights.


Creating a comprehensive data infrastructure

Wait…so.. I can have both?


Yes.. but only IF you NEED both.


It’s important to reiterate that Data Mesh and Data Fabric are not competing paradigms but actually complement one another. While each addresses different aspects of data management and integration, combined they can create a truly comprehensive and effective data infrastructure.


The main goal of Data Mesh is to enable better collaboration, data ownership, and data sharing across different domain teams in large-scale organizations. To achieve those goals, a Data Mesh focuses on the organizational and architectural aspects of managing data at scale, promoting decentralization, domain-oriented architecture, data as a product, and self-serve data platform capabilities.


On the other hand, a Data Fabric provides a unified and consistent view of data, making it easier for users to access, analyze, and use data across the organization. It focuses on addresses the challenges of data access, integration, and management across disparate sources


When you implement a Data Fabric first, having a Data Mesh becomes instantly easier and a natural add on. At the end of the day, the Data Fabric and Data Mesh can complement each other in the following ways:


  • Decentralized data management: a Data Fabric enables seamless data access and integration across various sources and domains, supporting the decentralized approach promoted by the Data Mesh.

  • Domain-oriented architecture: Data Fabric's ability to provide a unified view of data across disparate sources aligns well with Data Mesh's domain-oriented architecture, allowing domain teams to work independently while still having the ability to share and access data from other domains.

  • Self-serve data platform: The simplification of data access, integration, and management provided by a Data Fabric supports the self-serve data platform capabilities emphasized in the Data Mesh paradigm.

  • Data discoverability and governance: a Data Fabric can enhance data discoverability, governance, and compliance within a Data Mesh by offering metadata management, data cataloging, data lineage, and data quality features.


To conclude, the Data Mesh and Data Fabric are complementary concepts. As individual entities they offer unique qualities, the Data Mesh focusing on the organizational and architectural aspects, while the Data Fabric provides the underlying technological framework for effective data management and integration. Combined they can create a robust and efficient data infrastructure. By leveraging both paradigms, organizations can build a comprehensive data infrastructure that promotes collaboration, data ownership, and efficient data sharing across domains.


My take?


Implementing a Data Fabric product can be done in a fraction of the time, less than a day in most cases, than it typically takes to implement a Data Mesh. A Data Fabric can deliver value in the first week. But a Data Mesh can take 18+ months to implement and truly deliver value.


So, why not start with the Data Fabric first to see how the challenges around discovery, access, self-service, and security can be solved? Then, if your organization still requires the benefits of the Data Mesh, you can simply add to the Data Fabric as opposed to investing often millions of dollars and waiting for over a year hoping to see value.


To arrange a demonstration of Promethium’s Data Fabric product click here.




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