Data Fabric
Data Fabric is an architecture that integrates disparate data sources through intelligent connections and metadata, enabling seamless data access and processing across an organization's entire ecosystem.
Definition
Data Fabric is a contemporary architecture pattern that weaves together data from silos using metadata and intelligent integration. Unlike traditional data warehousing (which consolidates everything in one place), a data fabric leaves data where it is—in operational systems, cloud platforms, on-premises databases—and connects it via a unified metadata layer. This approach reduces the ETL burden, enables real-time access to source data, and allows organizations to work with data where it lives rather than moving it all to a central warehouse. Data Fabric leverages cloud, APIs, metadata management, and machine learning to discover relationships, auto-infer schemas, and recommend data assets.
How It Works
1. Sources: Data lives in many places (Salesforce, Snowflake, Postgres, S3). 2. Connectors: APIs and integration tools provide read/query access. 3. Metadata: A central metadata layer catalogs and indexes all sources. 4. Discovery: AI and search help users find relevant data. 5. Access: Users query across sources through a unified interface (virtual warehouse, lakehouse).
When to Use It
Data Fabric is ideal for organizations with complex, distributed data landscapes where building a monolithic warehouse is infeasible or where data must remain in source systems for latency or governance reasons. It's evolving concept, championed by vendors like Gartner. Consider if you have many cloud sources, federated governance needs, or real-time requirements.
Last updated: Jun 17, 2026