Gartner says a data fabric is a custom-made design that provides reusable data services, pipelines, semantic tiers, or APIs via a combination of data integration approaches in an orchestrated fashion. It can be made better by adding dynamic schema recognition or even cost-based optimization approaches. As a data fabric becomes increasingly involved or even introduces ML capabilities, it changes from a data fabric into a data mesh network.
Data fabric is a designed approach, mostly inclined toward use cases and locations on either “side” of a thread. The threads can cross and do handoffs in the centre or even reuse their parts, but they are not built dynamically. They are just highly reusable, normal services.
Characteristics of Data Fabric
Unified data access: a single, cohesive way to access data from multiple sources
Consolidated data protection: a consistent approach to data back-up, security, and recovery wherever the data is generated and stored
Centralized service level management: a single way of measuring and monitoring service levels related to responsiveness, availability, reliability of data.
Cloud mobility and portability: supporting the idea of a true hybrid cloud by minimizing the friction caused by collating and analyzing data from different cloud providers and apps.
Infrastructure resilience: by separating data management from specific technologies and putting it in a single, dedicated environment, a data fabric creates a more resilient system where emerging technologies or new data sources can be connected with minimal disruption.
A unified and flexible data ecosystem is critical and has a single view of your data, irrespective of the repositories generated, migrated to, or consumed from. The elasticity of a data fabric means it’s long-lasting, forward-looking, and beneficial, especially in a crisis. Check out data quality platforms like DQLabs that aids you in the whole data lifecycle for your organization or business.