Data Fabric
Data Fabric is an integrated, interconnected architecture that unifies diverse data sources, platforms, and tools to provide seamless access and movement of data across the organization.
A data fabric serves as the connective tissue across data systems: it abstracts away complexity of underlying platforms, enables data movement between heterogeneous systems, and provides a unified experience for discovering and accessing data. Rather than each team managing separate pipelines and data silos, a data fabric coordinates these systems with intelligent routing, caching, and transformation. It handles details like schema mapping (translating between source and target formats), metadata propagation (ensuring everyone knows what data exists), and quality guarantees (ensuring data is trustworthy across the fabric).
Data fabric is an evolution from traditional data integration platforms, enhanced with modern capabilities: it's cloud-native, supports both batch and real-time movement, includes AI/ML-powered metadata and lineage tracking, and adapts to changes in source systems automatically. Fabrics treat the organization's entire data estate as a unified resource rather than separate silos.
In practice, organizations build fabrics incrementally by layering technology: cloud data platforms provide the foundation, data integration tools (Fivetran, dbt, Airflow) form the arteries, metadata platforms (Collibra, DataHub) provide discovery, and governance tools enforce policies. A mature fabric enables analysts to query any data across the organization with a single interface, with all lineage, quality, and governance automatically managed.
Key Characteristics
- ▶Abstracts complexity of underlying systems and platforms
- ▶Provides unified metadata and discovery across data sources
- ▶Enables intelligent data routing and movement
- ▶Automatically handles schema mapping and transformation
- ▶Tracks data quality, lineage, and governance consistently
- ▶Supports both batch and real-time data movement
Why It Matters
- ▶Reduces time to access data by eliminating discovery burden
- ▶Reduces data duplication by providing seamless access to shared data
- ▶Improves data quality through consistent governance
- ▶Enables rapid onboarding of new data sources
- ▶Reduces total cost of ownership by consolidating tools
- ▶Supports compliance by centralizing audit trails and access control
Example
A financial services data fabric: core platform (Databricks) unifies internal databases, cloud storage, and SaaS platforms (Salesforce, ServiceNow). Integration layer automatically ingests data from all sources, metadata service catalogs everything with business definitions, governance engine enforces access policies, and analysts query through Looker which routes queries to appropriate sources. Adding a new data source (e.g., acquisition of company) requires registering it with the fabric once; all discovery, governance, and integration happen automatically.
Coginiti Perspective
Data fabric architectures depend on a semantic layer to make distributed data intelligible across the organization. Coginiti provides the governed abstraction that data fabric approaches require: a semantic layer defining consistent metrics and dimensions, an analytics catalog connecting logic to underlying data sources, and 21+ native connectors spanning the heterogeneous platforms that a fabric must integrate.
More in Core Data Architecture
Batch Processing
Batch Processing is the execution of computational jobs on large volumes of data in scheduled intervals, processing complete datasets at once rather than responding to individual requests.
Data Architecture
Data Architecture is the structural design of systems, tools, and processes that capture, store, process, and deliver data across an organization to support analytics and business operations.
Data Ecosystem
Data Ecosystem is the complete collection of interconnected data systems, platforms, tools, people, and processes that organizations use to collect, manage, analyze, and act on data.
Data Integration
Data Integration is the process of combining data from multiple heterogeneous sources into a unified, consistent format suitable for analysis or operational use.
Data Lifecycle
Data Lifecycle is the complete journey of data from creation or ingestion through processing, usage, governance, and eventual deletion or archival.
Data Mesh
Data Mesh is an organizational and technical paradigm that decentralizes data ownership to domain teams, each responsible for their data as a product, while using a shared infrastructure platform for connectivity and governance.
See Semantic Intelligence in Action
Coginiti operationalizes business meaning across your entire data estate.