Data Fabric vs. Data Mesh: Breaking Down the Differences Between the Two
Enterprise data is becoming increasingly more 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 and data mesh are two examples of the latest conceptual frameworks in data management. These frameworks both function differently and can seem abstract. Looking at a data mesh vs. a data fabric 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. This concept is an organizational paradigm, not a technology. This modern approach to data management relies 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
What Are the Main Differences Between the Two?
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:
- 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: Data is accessible in a data mesh through a controlled dataset. In a data fabric, data is available to users through 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.
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Explore our products to find out how you can implement data mesh concepts into your enterprise.