Data Strategy

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

Understanding the differences between data meshes and data fabrics will enable your organization to leverage both concepts to optimize your data management.

Organizations striving to optimize their productivity and strengthen their competitive positioning will do so faster by having better access to their data. Real-time access to relevant data will promote collaboration, enabling these teams to meet the organization's needs more effectively and thus ensuring that all of the information the business collects is put to good use.

Modern businesses are collecting more data than ever before. According to some estimates, the global data sphere will reach a size of 175 zettabytes (or 175 billion terabytes) by 2025. Compiling such massive amounts of data can quickly create data management challenges, making relevant information harder to access.

Fortunately, businesses can overcome the potential hurdle by building an agile, robust data management architecture. Two of the most prevalent approaches to doing so include data fabrics and data meshes. Though both approaches share similarities, there are key differences to understand before deciding what is best for your business.

What Is Data Mesh?

According to IBM, data mesh is described as "a decentralized data architecture" in which data is organized by specific business domains.” That is, sectors like customer service, sales, accounting, and marketing data would be organized into separate data sets.

This categorization is what makes a data mesh remarkable. This distributed architecture promotes migration to cloud-native technologies and solutions that enable organizations to scale their data management capabilities and increase the accessibility of their information tremendously.

Segmenting data based on business domains enables business leaders to manage their information more effectively. The organization can create detailed governance policies to preserve data quality, limit access to authorized personnel, and generate auditable trails for compliance purposes.

Benefits of Data Mesh

A data mesh framework can support the use of data warehouses, data lakes, or other traditional approaches to storage.

Gartner explains that:

"Data mesh represents a potential alternative or complement to centralization-only data management strategies for analytics and other reuse cases after first data capture in primary enterprise systems."

Data mesh architectures offer many benefits, including promoting better information governance and access control. Additionally, they eliminate many information logjams, which are commonplace when organizations manage their data using antiquated, centralized solutions.

Instead of relying on a centralized platform, an organization would have access to multiple repositories, each dedicated to a specific business domain or department. Data meshes also support the transition to cloud-native environments. Businesses can readily scale the data mesh framework as its data management needs evolve.

Many benefits associated with data mesh architectures appeal to large enterprises that manage incredibly complex data sets. That said, outside the data mesh approach, there may be more pragmatic options for smaller organizations handling less complex data.

What Is Data Fabric?

Data fabric is also a form of data management architecture. Unlike data mesh, it is designed to accommodate the end-to-end integration of various data pipelines. A data fabric architecture facilitates these integrations via automated systems and sophisticated intelligence tools.

The rise of edge computing devices, IoT, and machine learning have drastically increased organizations' abilities to harvest data. The explosive growth of data collection has made managing enterprise data even more challenging and complex.

A data fabric architecture enables data stewards to unite disparate applications and systems. Unifying disparate data repositories promotes better security, allows enterprises to protect consumers more effectively, and optimizes information accessibility.

Benefits of Data Fabric

A data fabric architecture allows enterprises to eliminate data silos, remove information bottlenecks, promote data accessibility, and foster collaboration between teams or departments.

Gartner explains that:

"Data fabric promises to elevate data management out of its silos of disconnected tools and datasets to an unprecedented level of integration, interoperability, and innovative business applications."

When data is siloed across a multitude of disparate systems, workers struggle to access the information they need to maximize their productivity. Additionally, data silos can create security risks, impede decision-making, and hinder the growth of an organization.

Implementing a data fabric framework also allows firms to take a more holistic approach to information analyses, yielding detailed insights that can guide decision-making and empower leaders to capitalize on emerging growth opportunities.

Data Mesh vs. Data Fabric

Given that the two concepts are unique and offer different sets of benefits to organizations that implement them, there is inevitably the "data mesh vs. data fabric" debate. In reality, these architectures are complementary solutions that can co-exist or even support the implementation of one another. They can be leveraged simultaneously, given the fundamental differences in how each architecture is implemented and what sort of changes both facilitate.

As our CTO and EVP of Innovation & Growth, Rex Ahlstrom, explains that data mesh is people driven. It relies on subject matter experts in data who are going in, tagging information and designing rules to create better awareness of how data impacts your business.

A data fabric approach is much more automated. It's less reliant on highly experienced data scientists as it uses AI/ML and is much more self-discovering regarding the insights it derives and the value it creates.

Finding the Right Data Management Approach

Each organization has unique data management needs and challenges, which will vary based on the size of the business in question, the industry it operates within, organizational goals and priorities, and resources.

Both data mesh and data fabric serve a broad range of purposes throughout an organization - and it's important to know that they aren't mutually exclusive. Enterprises can and do adopt both across certain - or all - departments.

Prior to deciding, first assess your organization's pain points, goals, and objectives. Then evaluate which concept or combination of concepts will best align with the needs of your business.

Using These Concepts to Optimize Data Management: Looking to the Future

As Rex observes in a recent blog post, more organizations are moving from a data mesh architecture to a data fabric approach.

“It's going to be more self-discovering in terms of the insights and value it creates. This design pattern is going to be increasingly important for companies to start adopting. That's definitely the direction I see things headed. If you're not moving toward an automated approach, you're not going to be able to keep up, and you won't be able to unlock all the potential value in your systems and data. “

For our take on which technology will edge out the other, check out Rex's emerging data trends and predictions for 2023.

Data fabric, data meshes, or some combination of the two will play a role in addressing your organization's information management challenges both now and in the future. Learn more at

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