Connect legacy systems
The enterprise connects CRM, billing, orders, support, and product usage systems into a shared fabric layer.
Page 8 Future Trend
Real-Time Enterprise Data Fabric
A real-time enterprise data fabric is a unified data access and integration layer that connects databases, applications, APIs, event streams, warehouses, lakes, and AI systems so business data can be discovered, governed, synchronized, and used across the enterprise.
Unified access layer
Real-time synchronization
Governed data sharing
AI-ready context
Definition
What it means
A real-time enterprise data fabric helps organizations avoid isolated data silos by creating a connected layer across operational databases, SaaS platforms, analytics systems, cloud storage, and AI tools. It can move data, virtualize data, catalog data, govern data, and stream data changes depending on the business need.
In database modernization, the data fabric becomes the connective tissue between legacy systems and new cloud architectures. Teams can migrate gradually while maintaining real-time access to critical records, reducing downtime, improving lineage, and giving AI agents a governed view of enterprise data.
Core Capabilities
What it enables
- Connects on-premise, cloud, SaaS, warehouse, and lakehouse data
- Supports real-time change data capture and event streaming
- Provides governed access through catalogs, policies, and lineage
- Allows phased migration without stopping business operations
- Combines physical movement, virtualization, and API-based access
- Gives analytics and AI tools a consistent enterprise data layer
- Improves resilience by reducing point-to-point integration sprawl
Use Case
Keeping customer data live during migration
Stream changes
Change data capture keeps the new customer database synchronized while the old systems continue running.
Govern access
Data catalog, lineage, access policies, and compliance rules control which teams and AI tools can use sensitive customer data.
Cut over safely
Teams validate real-time consistency, shift applications gradually, and retire legacy sources only after downstream systems are stable.
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