Data Fabric vs. Data Mesh: What’s the Difference, and What’s Right for You?

Michele Goetz, vice president and principal analyst with Forrester, describes data fabric as a technology layer that allows for data decentralization. “Data fabric is specifically the technology platform,” she says. “It’s about the tools and capabilities that you’re running your data on, and it’s the ability to take advantage of your data, even when it’s distributed.”

In his personal commentary on the topic, Sandipan Sarkar, a distinguished engineer of hybrid cloud transformation with IBM Global Business Services, describes data fabric as “a design or architectural pattern” that helps to ensure that data is available wherever it is needed.

“It is distributed and heterogeneous architecture,” Sarkar says. “It consists of nodes that can be transactional, such as an application database or a data stream, or analytical, like a data warehouse or data lake.” 

Sarkar, who co-authored a white paper on integrating data fabric into a hybrid multicloud environment, adds that data fabric allows for the delivery of data at two levels: how the data is managed, and how the data is integrated. Data fabric, he says, allows data to be managed through a marketplace of information that is acquired based on needs.

“As a product, data is discoverable through a metadata search and knowledge graph. The consumers — inside or outside the enterprise — may come and shop for the product that they are interested in,” he says. “They would not bother with how the data is prepared. They are simply focused on easily consuming it.”

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What Does Data Mesh Offer as a Data Management Strategy?

Data mesh, on the other hand, is an application layer on top of data that distributes relevant information to the desired audience quickly, effectively creating a context around the data’s eventual use case.

“Data mesh is an approach that brings process and technology together more easily and effectively to concentrate on the people, process and technology,” Goetz says.

Imagine, for example, an organization with many different departments that are looking to access data internally. The HR department may not have the same needs as the marketing department. A data mesh approach would allow each department to access data resources based on its business needs, and choose those data packages as “products” rather than having the data access being deeply integrated into the architecture.

“Data mesh really focuses on helping you home in on the domain that matters, which is in the context of your business,” Goetz says. “This then allows you to figure out how you shape that data, define it and apply the right policy.” 

Data Fabric vs. Data Mesh: What’s the Difference?

In many ways, data fabric and data mesh reflect two levels of technical maturity and work at different levels within a business or organization. 

If data fabric is about getting data to the right place, data mesh gets that data to the right place with the right context. 

Often this approach is described as a “productization” of data, which Sarkar suggests can help resolve complexity issues that emerge when data is treated as an underpinning technology.

“With the product culture, consumers in the enterprise need not worry about the complex engineering of data,” he says. “They simply go and shop and consume. Like they would with a product in a marketplace, they can check the currency, authenticity and value of the data, which is underpinned by solid data governance techniques.”

The result is that data fabric and data mesh are not so much competing approaches to data management as they are complementary, with data fabric often creating underlying architecture to allow for data mesh.

“Data mesh makes you ask the question, ‘What do I want to do with my data? How’s it going to help me answer a question? What insight do I need? What data is important? And how do I bring that together?’” Goetz says. “And then you can start thinking about the technologies that allow you to do that, that might be using data fabric architectural capabilities, or you might be using other places that have updated capabilities and tools to make that happen.”

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The Benefits of Data Decentralization

So, you might be wondering: Can data fabric or data mesh help my business? And if so, how? IBM’s Sarkar believes the benefits of data fabric and data mesh won’t be felt as keenly in organizations that are smaller and more centralized.

Forrester’s Goetz, however, argues that decentralized data approaches like these can work for any industry, based not on organizational needs but on operational maturity. Not every organization is ready to take on data mesh just yet, but data fabric can be easier to implement.

“Everybody is going to be data driven. It’s not relegated to just the analytics team or a data scientist or consultant,” she says. “It’s going to be woven into the fabric of how businesses operate.”

If you’re thinking about a data fabric or data mesh strategy, an analysis of your infrastructure by an outside organization like CDW Amplified™ services might be a great place to start. It could lay the groundwork to ensure your organization keeps up with its many growing pieces.