Data fabric refers to the unified data management architecture and the set of capabilities that provide consistent capabilities to conveniently connect data endpoints and enable end-to-end data management capabilities.

Let’s take a look at the data fabric architecture.

What is data fabric?

Data assets are generated in silos and hidden across the hybrid mix of infrastructure environments. Data preparation cycles are long, and users need a broad set of data management capabilities to overcome the limitations facing complex multi-vendor, multi-cloud, and evolving data environments.

The data fabric architecture is designed specifically to address the challenges facing the complex hybrid data landscape.

Essentially, data fabric can be described as a converged platform supporting the diverse data management needs to deliver the right IT service levels across all disparate data sources and infrastructure types. It operates as a consolidated framework to manage, move, and protect data across multiple isolated and incompatible data center deployments.

As a result, organizations can invest in infrastructure solutions that align with their business requirements—without concerns surrounding data service levels, access, and security.

Data fabric capabilities & principles

While traditional data management concepts such as DataOps are focused on the operationalization of large and distributed data assets, the Data Fabric is focused on capabilities that unify diverse and distributed data assets.

In simple terms, most organizations adopt frameworks such as DataOps to design, implement, and maintain a distributed data architecture. It helps make sense of data that is generated and maintained in a highly distributed infrastructure environment. By introducing a unified data management platform architecture such as Data Fabric, the end-to-end data management processes are combined, specifically:

• Data integration
• Data discovery
• Data governance
• Data curation
• Data orchestration

(Learn about BMC’s approach to DataOps.)

All tasks are managed within a single platform architecture designed to simplify access, management, and control over distributed data assets. A Data Fabric can include an array of data management capabilities across the following logical domains:

Knowledge, insights & semantics

• Semantic layers of descriptions that enable users to discover and access relevant data
• Access to a vast pool of data assets in the form of a marketplace
• Continuous analytics over growing data assets
• Use of advanced AI systems to connect business relationships between data across disparate applications.
• End-to-end data management visibility to measure various attributes and risk associated with data

Unified governance & compliance

• Local management of metadata in compliance with global organizational policies that can be applied to all data assets
• Automation makes it easier to apply policies, audit compliance, and identify potential breaches in systems
• Automation and AI capabilities augment data tracing and route querying
• The overall data governance and security process is centralized and consistent across all environments

Intelligent integration

• The design, deployment, and utilization are integrated across distributed data and infrastructure environments
• Automated flow and pipeline creation for the siloed data environments
• Optimal workload distribution and correction of schema drifts
• Self-service ingestion of new data assets within predefined policies
• Future proofs infrastructure; agnostic to platform and applications

Orchestration & lifecycle

• Self-service orchestration of disparate data sources using advanced AI systems, data lakes, and other platforms and technologies that ensure a comprehensive view of the data pipeline across all data environments.
• Unified data lifecycle to configure and manage all aspects of the data including development, operations, testing, and production release of data-driven applications.