Data Fabric vs. Data Mesh
While they share common goals to manage data across an organization, Data Fabric and Data Mesh are quite different.
Contrasting Approaches of Data Mesh and Data Fabric Architectures
Data mesh is focused on delivering domain specific data to teams so that they can build their own data products.
Both data mesh and fabric architectures serve as frameworks for a seamless, integrated data experience across complicated, distributed systems. However, their approaches differ in several fundamental ways.
- Data mesh adopts a product-centric view of data as its core design principle. This approach ensures that data is managed, maintained, and provisioned like any other product within the organization. On the flip side, data fabric utilizes automation for tasks like discovering, connecting, and delivering data assets, all based on a comprehensive metadata foundation, often represented by a knowledge graph.
- The governance models in the two architectures are different. In a data mesh, domain or product owners are responsible for establishing initial requirements for data products, ensuring a more hands-on, domain-specific approach to data management. Data fabric leans more on automation and metadata to cater to data consumer needs, with less emphasis on human-led governance.
- The organizational requirements also vary significantly. Unlike data fabric, data mesh requires technological changes and demands organizational structure and culture shift. It places the responsibility of data product delivery onto domain teams, potentially adding to their existing workload. Additionally, it promotes the decentralization of data roles, such as data scientists and engineers, embedding them within domain or product teams.
Design First
Decentralized approach with data divided among different domain
Unified approach with data from disparate sources integrated into a single layer
Data Management
Each domain owns and manages its own data, potentially increase scalability and granularity of data
Data from diverse sources are seamlessly integrated and easily accessible, regardless of its location or application
Data Governance
Data governance is distribute, with each domain taking responsibility of its own data quality and standardization
Requires a sophisticated data integration and governance mechanism to ensure seamless data flow and high-quality data
Benefits
Better granularity and ownership of data, potentially leading to faster insights
Seamless data access, improved data integration, enhanced data governance, and scalability
Challenges
Increased complexity in data modeling and potential increase in infrastructure costs. Standardization and data quality can be challenging due to distributed ownership
Need for a sophisticated data integration and governance mechanism to ensure seamless data flow and high-quality data
