Glossary

Data Fabric

A data fabric is an architecture that connects data across different storage systems, applications, clouds and locations so it can be accessed, governed and moved more consistently. Rather than requiring organizations to consolidate all data into one repository, a data fabric creates a common layer for managing distributed data.

The concept has become increasingly relevant as enterprise data spreads across data centers, public clouds, edge environments, object storage, file systems, databases and AI infrastructure. A data fabric helps organizations work with this distributed data while maintaining policies for security, governance, availability and lifecycle management.

How does a data fabric work?

A data fabric does not refer to a single product or storage system. It is an architectural approach built from multiple technologies that coordinate how data is discovered, accessed, protected and managed.

Depending on the implementation, a data fabric can include:

• Data catalogs and metadata management
• APIs and standardized data access
• Data integration and orchestration
• Policy-based data management
• Identity and access controls
• Data movement and replication
• Observability and monitoring
• Governance and compliance controls
• Storage across on-premises, cloud and edge environments

Metadata plays an important role. Information about where data resides, who owns it, how it is classified and which policies apply can help the fabric determine how data should be handled without requiring administrators to manage every dataset independently.

Some data fabrics also use automation to apply policies or coordinate data placement as requirements change.

Why do enterprises use data fabrics?

Large organizations rarely have their data in one place. A single enterprise may operate several data centers, multiple cloud platforms, hundreds of applications and many types of storage infrastructure.

This creates fragmentation. Teams may struggle to locate datasets, apply consistent security policies or make information available to new applications without creating additional copies.

A data fabric provides an architectural framework for connecting those environments. The goal is not necessarily to eliminate different platforms, but to make distributed data easier to use and manage as a whole.

This can be particularly useful for organizations pursuing hybrid cloud, data sovereignty or AI initiatives because these environments frequently require data to remain distributed for cost, performance, regulatory or operational reasons.

Data fabric and storage infrastructure

Storage is one of the foundational layers beneath a data fabric. Applications can only access distributed information effectively if the underlying infrastructure can store, protect and serve that data at the required scale.

Object storage is particularly relevant for data fabrics containing large volumes of unstructured data. Object systems can provide a common S3-based access model for datasets such as documents, images, video, backups, logs, analytics data and AI training content.

Large-scale object storage can also reduce the need to divide unstructured data into many isolated storage silos. A common storage layer can make it easier for applications and data services to work with information across its lifecycle.

However, storage alone does not constitute a data fabric. Cataloging, governance, integration, orchestration and policy management are separate architectural capabilities.

What is the role of data fabric in AI?

AI is increasing interest in data fabric architectures because AI systems depend on access to data created by many different applications and business processes.

Training, retrieval-augmented generation (RAG), analytics and inference workflows may need information stored across object repositories, databases, file systems, archives and cloud services. Moving every dataset into a dedicated AI environment can create unnecessary duplication and infrastructure costs.

A data fabric can help organizations discover and connect relevant datasets while maintaining information about ownership, classification and governance. Storage infrastructure beneath the fabric then determines how efficiently large datasets can be retained, moved and accessed.

For enterprise AI, this distinction matters: the data fabric helps connect and govern the data estate, while the storage architecture provides the capacity, durability and performance required to persist the underlying datasets.

Data fabric vs. data mesh

Data fabric and data mesh address some of the same problems but approach them differently.

A data fabric emphasizes the technology and architecture used to connect distributed data. It typically focuses on integration, metadata, automation, governance and consistent access across heterogeneous systems.

A data mesh is primarily an organizational and ownership model. It treats data as a product and distributes responsibility for datasets to individual business domains rather than concentrating ownership within a central data team.

The approaches are not mutually exclusive. An organization can use data mesh principles for ownership while using data fabric technologies to connect and govern the resulting distributed datasets.

Data fabric vs. data lake

A data lake is a repository designed to store large quantities of structured, semi-structured and unstructured data. A data fabric is the broader architecture used to connect and manage data across multiple repositories and environments.

A data lake can therefore be one component within a data fabric.

This distinction is important because building a data fabric does not require moving every dataset into a central data lake. Some information may remain in operational databases, object storage clusters, cloud services or regional infrastructure while still participating in the broader architecture.

Data fabric vs. data virtualization

Data virtualization creates a logical access layer that allows applications or users to query data from multiple sources without necessarily moving it into a new repository.

A data fabric is broader. Virtualization may provide one method of accessing distributed information, while a data fabric can additionally address metadata, governance, security, orchestration, data movement and lifecycle policies.

Data virtualization can therefore function as one technology within a data fabric architecture.

What are the benefits of a data fabric?

A well-designed data fabric can improve how enterprises work with increasingly distributed information.

• Consistent data access: Applications and users can interact with information across heterogeneous systems through more standardized interfaces.
• Reduced data silos: Existing repositories can remain in place while becoming part of a broader data architecture.
• Improved governance: Metadata and policy frameworks can help organizations apply security, retention and compliance requirements more consistently.
• Greater data mobility: Data can be replicated or moved between environments when business, performance or regulatory requirements change.
• Hybrid and multi-cloud flexibility: Organizations can manage data spanning private infrastructure, public clouds and edge locations without treating each environment as completely separate.
• Better AI data accessibility: AI pipelines can discover and use relevant enterprise datasets without requiring every source to be manually consolidated first.

What are the challenges of implementing a data fabric?

The breadth of a data fabric is also what makes implementation difficult. Enterprises may need to integrate storage platforms, databases, applications and cloud services built at different times and with different interfaces.

Metadata quality is another challenge. Automation and governance become less effective when organizations do not know what data they possess, where it originated or which policies should apply.

Organizations also need to avoid creating a new centralized bottleneck. If every application becomes dependent on a proprietary integration layer, the data fabric itself can become another source of complexity or lock-in.

For this reason, interoperability and widely adopted interfaces such as S3 APIs can be important considerations when designing the underlying architecture.

Data fabric and data sovereignty

A data fabric can provide logical access to distributed information without requiring all physical data to reside in the same location.

This capability is useful where organizations must control where particular datasets are stored. Data may remain within a country, region, private cloud or on-premises environment while still being discoverable and governed through the broader data architecture.

The fabric does not automatically provide data sovereignty. Residency controls, encryption, access management and infrastructure ownership must still be implemented at the storage and platform layers.

What should organizations consider when building a data fabric?

Organizations should begin with the data they need to connect rather than attempting to deploy a fabric across the entire enterprise at once. Identifying important datasets, applications and workflows can expose which integration and governance capabilities are actually required.

The underlying infrastructure should also support the expected scale of the data estate. For environments dominated by unstructured data, considerations include capacity scalability, metadata scalability, durability, S3 compatibility, data mobility, security and the ability to operate across multiple locations.

Open interfaces are particularly valuable because a data fabric is inherently heterogeneous. Infrastructure that can interact with multiple applications and ecosystems gives organizations more flexibility as data architectures evolve.

How does object storage support a data fabric?

Enterprise object storage can provide a scalable persistence layer for the unstructured datasets connected through a data fabric.

S3-compatible access allows many applications, analytics platforms and AI tools to interact with a common storage architecture. Object metadata can provide additional context for discovering, classifying and managing datasets, while replication and lifecycle capabilities can support data placement across locations.

At large scale, object storage can also provide an alternative to maintaining separate infrastructure for every new application or dataset.

Scality RING is designed for large-scale enterprise file and object data, including environments where organizations need to retain and access substantial volumes of distributed unstructured data. Within a broader data fabric architecture, storage platforms such as RING provide the persistent data layer while catalog, governance and orchestration technologies provide the higher-level fabric capabilities.

Frequently asked questions

Is a data fabric a database?

No. A data fabric is an architectural approach that connects multiple data systems. Databases can participate in a data fabric alongside object storage, file systems, data lakes, cloud services and other repositories.

Does a data fabric move data?

It can. Some data fabric implementations use virtualization to access data where it already resides, while others replicate, transform or move datasets between environments. Most enterprise architectures use a combination of approaches.

Does a data fabric replace a data lake?

No. A data lake can operate as one repository within a broader data fabric. The fabric provides connectivity and management across the data lake and other data sources.

What is an enterprise data fabric?

An enterprise data fabric applies data fabric principles across an organization's broader data estate. It typically connects multiple applications, repositories, clouds and locations while providing common capabilities for access, metadata, governance and policy management.

Why is data fabric important for AI?

AI applications frequently require data from many sources. A data fabric can make those datasets easier to discover, govern and access while allowing organizations to keep data distributed when consolidation is impractical.

What is the difference between data fabric and data mesh?

Data fabric primarily describes a technical architecture for connecting and managing distributed data. Data mesh primarily describes an organizational model in which business domains take responsibility for their own data products. Organizations can use both approaches together.

Summary

A data fabric connects distributed enterprise data through a common architecture for access, metadata, governance and management. It allows data to remain across different storage systems, clouds and locations while making those environments easier to use collectively.

As enterprises accumulate more unstructured data and build hybrid cloud and AI infrastructure, the ability to connect data without constantly consolidating it becomes increasingly valuable. Scalable object storage can provide the persistent data foundation beneath a data fabric, while integration, cataloging and governance technologies coordinate how that data is discovered and used.