Blending Data Mesh and Data Fabric: Crafting a Balanced Data Strategy

Written by Practitioners for Practitioners

This month’s newsletter is focused on data mesh and data fabric, subjects that Eckerson Group has been researching and writing about since they each took hold of the data world. We zero in on how these two strategies have evolved together to offer synergistic solutions for modern data challenges.

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Visit our Data Mesh & Data Fabric topic page to find numerous reports, webinars, and articles on both of these approaches to understand, plan, and implement them for your organization.

Many practitioners view data mesh and data fabric as mutually exclusive approaches to data strategy. However, these paradigms complement each other. Data mesh focuses on decentralization and autonomy; Data fabric ensures centralized integration and governance. This article explores how blending elements of both can offer flexibility and control to create the right fit for your organization’s data strategy.

Data Mesh vs. Data Fabric: Comparing the Paradigms

Centralization vs. Decentralization

Data Fabric is a centralized, technology-driven solution aimed at creating a unified platform for managing and accessing data wherever it resides. The fabric focuses on automating data integration across sources and provides a consistent data infrastructure, leveraging AI and metadata-driven architectures to enable real-time analytics. It provides seamless access to data through an abstraction layer that hides the underlying complexity of diverse data sources.

Data Mesh, on the other hand, decentralizes data and its ownership. In a data mesh, individual teams or business units are responsible for their own data and are charged with creating "data products," for their own consumption and presumably the consumption of others in the organization. The data mesh ensures that data is available, discoverable, and usable by any one while federated governance, another pillar of the methodology, enforces standards across the organization.

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There’s a lot more to understand about data mesh and data fabric. Get the full story on data mesh in our report, Practical Approaches to Implementing a Data Mesh . Learn how its four pillars (domain ownership, data as a product, the self-serve data platform, and federated computational governance) are the foundation for a very different approach to managing data for analytics.

Dive deeper into data fabric with Data Fabric: The Next Step in the Evolution of Data Architectures to see how it uses metadata, machine learning, and automation to provide a unified view of enterprise data regardless of its format or location.

The diagram below illustrates the main components of each approach. It shows fabric’s technical components that provide a unified platform from data sources to data consumers, while data mesh consists of a set of decentralizing organizational and operational concepts emphasizing domain autonomy.

Advantages and Disadvantages

Their differences in approach come with different advantages and disadvantages. The table below outlines the trade-offs between the two paradigms.

Blending Mesh and Fabric: A Solution Spectrum

With an understanding of the trade-offs that mesh and fabric entail, organizations need to find the right balance between central control and decentralized autonomy. This balance, driven by their needs, data maturity, and business model, results in a unique blend of elements from both.

It’s helpful to frame data mesh and data fabric as part of a solution spectrum. On one end is data mesh with its independent functions, such as defining domain data terms and meaning and ensuring data assets are relevant and reliable to the business. On the other end is data fabric with its shared functions, such as data access control and a common interface for finding and consuming data assets.

Determining Your Organization’s Place on the Spectrum

This mental model offers organizations a framework to strategically blend the benefits of control and autonomy. Finding the right place on the mesh-fabric spectrum depends on several factors:

• Regulatory and Compliance Requirements. Highly regulated industries, like finance and healthcare, require strong shared governance functions to ensure compliance and data security. Organizations in these sectors may emphasize centralized control to enforce consistent standards while allowing some domain autonomy for specific business needs.
• Cultural Readiness. Organizations with a culture that values autonomy, ownership, and collaboration will find it easier to adopt independent domain-level functions associated with data mesh. Companies that prioritize top-down decision-making often resist incorporating independent functions that enable domain agility making data fabric a more natural fit.
• Data Maturity. Organizations with mature data governance frameworks can be better positioned for independent domain-level functions, allowing domain experts to own and manage data assets. Less mature organizations may need stronger centralized controls to ensure quality, consistency, and scalability as they build their data capabilities.
• Business Agility. Fast-moving industries, like tech or ecommerce, require greater domain autonomy to enable rapid innovation. These organizations can adopt decentralized data management to align more closely with dynamic business needs while maintaining some centralized functions to support discoverability and cross-domain data usage.
• Scale and Complexity. Large organizations with complex data environments may need to rely on centralized functions for standardizing key data practices across business units, such as defining data quality benchmarks or ensuring access to shared infrastructure. At the same time, they will need independent domain-specific functions to ensure relevance and responsiveness to individual business units.

Data Products: A Common Thread

Once a company determines how its needs define an optimal balance of control and autonomy, it needs a practical way to implement it. Enter data products. The concept of a data product ties data mesh and data fabric together. A data product is a data asset that has all the characteristics of something that can be consumed by people you don’t know. It must be standardized, packaged, shoppable, deliverable, and returnable. Creating data products that meet this standard requires both shared and independent functions.

Shared Functions of Data Products

The following shared functions ensure that while each domain operates independently, their data products remain aligned with the broader organizational standards and practices, ensuring consistency and trust:

• Data Quality Standards. Central teams define and enforce organization-wide data quality benchmarks to ensure that all data products meet a minimum threshold of reliability.
• Data Discoverability. Centralized metadata management and data catalogs make data products accessible and discoverable across the organization, preventing data silos.
• Security and Compliance. A shared governance enforcement layer ensures that all data products adhere to security protocols, privacy regulations, and compliance standards.
• Interoperability. Centralized governance enforces data interoperability standards, allowing seamless integration and use of data products across domains.

• Data storage. Domains get carve outs in an enterprise data platform where they can mix and match their own data with enterprise data to build products without impacting other domains.

Independent Functions of Data Products

The following independent functions allow business domains to innovate and make faster decisions:

• Domain-Specific Data Terms and Models. Each domain defines its own data terms, schemas, and models, ensuring that the data products are tailored to their specific business needs.
• Data Relevance and Context. Domain teams curate data products to ensure they remain relevant and aligned with the fast-changing demands of their respective areas, enabling more accurate decision-making.

• Local Data Operations. Domains control how they process, clean, transform, and store data, giving them the autonomy to iterate quickly and adapt to business changes without relying on central teams.

Learn More

Delve into the relationship between data products and the complementary principles of data mesh and data fabric in our CDO TechVent: Building Data Products with Data Mesh & Data Fabric.

The recording of this 3-hour virtual event and associated Market Landscape Report helps data leaders evaluate and select data mesh and data fabric tools that deliver and manage discoverable, addressable, and trusted data products. The event compresses the time it takes data leaders to understand an emerging technology, create a short list of products, and hear tips from experts, practitioners, and solutions providers in the field.

Resources:

• Event recording & Slides
• Market Landscape Report

The Data Marketplace: Where the Balancing Act Happens ?

A data marketplace is where organizations implement their balance of both shared and independent functions. It allows users to browse, acquire, and consume data products from different domains with rich metadata that allows them to understand, evaluate, and access them for their business needs.

The marketplace enables shared governance by providing standardized tools for data discoverability, quality control, and data access control, ensuring that all data products meet organizational standards. It empowers individual domains by allowing them to create, manage, and publish data products tailored to their specific business needs but that may also be useful to other domains.

This diagram illustrates how the data marketplace is part of a common data platform that enables domains to create data products managed by shared data standards.

Establish a Foundation

No matter where you land on the solution spectrum, it’s critical to establish a solid foundation for future growth. Key foundational elements include:

• Governance: A strong governance framework, whether centralized or federated, ensures data quality, security, and compliance across the organization.
• Metadata Management: For both fabric and mesh, metadata plays a vital role in making data discoverable and interoperable.

• Data Culture: Fostering a culture that values data as a product and encourages collaboration across teams is essential to success in both paradigms.

Building an Adaptive Data Ecosystem for Long-Term Success

As data management continues to evolve, the tension between centralized control and decentralized autonomy will grow, offering new opportunities for innovation. The future of data strategy lies in adaptability—being able to dynamically shift as business needs change, technology advances, and data complexity grows. Embracing a hybrid approach now sets the stage for scalable, agile, and intelligent data systems that can power the next generation of decision-making. Organizations that view their data strategy not as a permanent choice but as a flexible framework will thrive.

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We’re not the only ones who think data mesh and data fabric work together. Kaycee Lai, CEO of Promethium , offers a straightforward and practical perspective in his article Data Fabric and Data Mesh: Complementary Frameworks for a Unified Data Architecture.

Visit our Data Mesh & Data Fabric topic page for broad coverage of both approaches.

Upcoming Webinar on Data Mesh and Data Fabric

At Eckerson Group, we believe Data Mesh and Data Fabric are not replacements for traditional architectures but vital additions. They work together—Data Fabric as infrastructure and Data Mesh as a distributed development approach—to support flexible, scalable data environments. Wayne Eckerson and Jay Piscioneri will dive deep into how these paradigms augment your existing data strategy while retaining the core strengths of data warehouses and lakes.

Join us on October 31 at 1 p.m. Eastern.

Sincerely,

Jay Piscioneri,

Senior Consultant, Eckerson Group

Evolve your data strategy with Data Fabric, Data Mesh, or both.

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