Data Mesh and Data Fabric

What is data mesh?

Data mesh is a methodology of managing data, whereby instead of one central data control unit or team, data management is decentralized in an organization. Different functional areas manage their data and make it available to other teams. A data mesh is a set of principles for designing a modern, distributed data architecture that focuses on these business domains.  It emphasizes decentralized ownership, standardization, and collaboration.

What is a data fabric?

Data fabric however is an architecture design presented as an integration and orchestration layer built on top of multiple, disjointed data sources like relational databases, data warehouses, data lakes, data marts, IoT, legacy systems, etc., to provide a unified view of all enterprise data. Metadata drives the fabric design. Data fabric is as much methodology as technology, and it can be designed and deployed manually or automatically. A data mesh and data fabric approach can coexist in an organisation.

Data mesh vs. data fabric: What’s the difference?

Data mesh is a methodology whereby data management is decentralized in an organization. Different functional areas manage their data and make it available to other teams. Data fabric, on the other hand, is a centralized architecture that connects and integrates data from various sources to provide a unified view. It uses metadata and automation to simplify data access and management.

In short, data mesh decentralizes data ownership, while data fabric centralizes data integration—and both can work together in a modern data strategy.

Solidatus ensures the successful implementation and execution of data mesh and data fabric methodologies. It offers a detailed single source of truth for business and technical teams to understand their organisation’s data management practices and how different teams use data – bringing the business and technology together. Using a detailed data lineage tool across the whole organization supports collaboration, management, troubleshooting, and impact assessment, particularly when data is managed in a federated way.