Data Fabric vs. Data Mesh: Breaking Down the Differences Between the Two

Enterprise data is becoming increasingly complex as organizations collect unstructured data from various sources. As data management demands continue to grow, more frameworks attempt to address the biggest challenges in managing unstructured data. Data fabric vs. data mesh are two examples of the latest conceptual frameworks in data management. These frameworks both function differently and can seem abstract. Comparing data fabric vs. data mesh can help enterprises understand which framework may best suit their needs.
‍

What Is Data Fabric?

A data fabric is a centralized data platform architecture that uses a layer of technology to separate data from applications. The data fabric framework leverages automation to collect data from various sources and extract insights from the information collected.

When implemented, a data fabric will continuously query and analyze your metadata. This process allows the data fabric to identify connections between datasets without point-to-point integrations between applications. This architecture also enables you to set universal controls for managing permissions and compliance standards.

What Is Data Mesh?

A data mesh is a data platform architecture that uses a domain-based, self-service design to make data accessible. Unlike data fabric, data mesh is an organizational paradigm rather than a technology. This approach to data management is built on four pillars:

• Distributing the ownership of data
• Creating a self-service infrastructure to make data accessible for anyone who needs it
• Treating data as a product
• Federating data governance to create universal standards for quality, discoverability, and schema

Data Fabric vs. Data Mesh: What Are the Main Differences?

While data fabric and data mesh have the shared goal of simplifying data management, these concepts are different. Data fabric is a conceptual deep technology, and data mesh is a conceptual framework.

Data fabric relies on curated technology stacks to connect data sources, extract data via automation, and deliver data to end users. Data mesh uses smaller, specialized domains within an organization and relies on a self-service model, rather than automation.

Beyond this architectural difference, these concepts have other key differences. Here are key differences between Data Fabric vs. Data Mesh:

• Centralization: Data mesh is decentralized. It uses multiple domains with their own functions to make data more accessible to a user. Data fabric is centralized. All data is managed from a single, virtual system.
• Access: In a data mesh, data is accessible through a controlled dataset. In a data fabric, data is available to users via Software Development Kits (SDKs) or application programming interfaces (APIs).
• Governance: In a data mesh, governance involves every domain when creating guidelines and policies within the workflow. A data fabric typically relies on a single authority to create and enforce the data policies and guidelines.

The Advantages of Using Data Mesh

Data mesh is a modern technique for data management, and it offers several benefits to organizations. Implementing a data mesh leads to:

• Improved data governance: With multiple domains managing data policies and guidelines, data meshes are more democratic, and policies can cater more to individual needs within a given domain.  
• Enhanced data quality: As each domain manages its subsets of data, they have a greater level of control over data access, updates, and changes. With this improved connection to data, these domains can ensure data quality more effectively.
• Greater resilience and scalability: Centralized data poses the risk of losing data during outages or facing bigger challenges in scaling. Data mesh allows other domains to remain untouched in the midst of an outage. When scaling is needed, adding more domains to the workflow is more manageable than expanding a centralized system.

Advantages of Data Fabric

For organizations that prefer a more centralized approach, data fabric offers significant advantages:

• Centralized Control: All data is managed from a single, virtual platform, making it easier to enforce compliance and ensure data integrity across the board.
• High Automation: With built-in automation, data fabric is excellent for enterprises that need to streamline complex data integration processes.
• Universal Data Access: By using APIs and SDKs, data can be accessed by multiple applications without the need for domain-specific controls, making it a more straightforward solution for data-driven decision-making.

Which Is Right for Your Organization?

When deciding between data fabric vs data mesh, consider your organization’s structure and data needs. If you have a highly regulated environment or need centralized control with advanced automation, data fabric may be the best option. However, if your organization operates with multiple teams or departments that require autonomy over their data, data mesh may offer the flexibility you need for data governance and scalability.

Learn About RightData’s Data Management Solutions

At RightData, we have unlocked the power of data mesh with DataFactory — our self-service, no-code data integration platform. We empower enterprises to transform raw data into insights. DataTrust allows users to improve data quality, reliability, and completeness.

Get a demo to find out how you can implement data mesh concepts into your enterprise.