A data fabric is a modern data architecture designed to democratize data access across an organization. It uses intelligent and automated systems to break down silos, manage data assets and optimize data management at scale.
Over the past decade, advancements in hybrid cloud, artificial intelligence, the Internet of Things (IoT) and edge computing have driven the exponential growth of big data. This surge has created increasingly complex data environments, with vast volumes of data scattered across disparate business units.
According to a 2025 study from the IBM Institute for Business Value (IBV), 50% of CEOs say their organization has disconnected technology due to the pace of recent investments. As a result, data unification and governance have become critical to overcoming challenges such as data silos, security risks and decision-making bottlenecks.
A data fabric offers integrated, end-to-end data management that is supported by machine learning (ML), active metadata, application programming interfaces (APIs) and other technologies.
It is not a piece of software, but rather a design approach that creates a unified view of data across an organization’s on-premises and multicloud environments, from data lakes, data warehouses, SQL databases and other sources. With this approach, organizations don’t have to move distributed data to a single location or data store, nor do they have to take a completely decentralized approach.
These core capabilities not only address data silos and growing data volumes, but also enable simple, self-service data access for business users. The result is a network of real-time data and high-quality historical data that accelerates digital transformation and business intelligence (BI) initiatives across the businesses, while automated governance ensures a secure and compliant data strategy.
For many organizations, explosive data growth (of structured, semi-structured and unstructured data) has overwhelmed traditional data management approaches. This challenge is intensified by the proliferation of data warehouses, data lakes and hybrid cloud environments.
These storage systems are typically leveraged as low-cost solutions for large amounts of data. However, they often lack proper metadata management, making data difficult to locate, interpret and use effectively.
Siloed data adds to this complexity. Historically, an enterprise might have separate data platforms for HR, supply chain and customer information, each operating in isolation despite overlapping data types and needs.
These challenges lead to huge accumulations of dark data—information that is neglected, considered unreliable and ultimately goes unused. In fact, an estimated 60% of enterprise data remains unanalyzed.1
Businesses use data fabrics to address these challenges. The modern architecture unifies data, automates governance and enables self-service data access at scale. By connecting data across disparate systems, data fabrics empower decision-makers to make connections that were previously hidden and derive more valuable business outcomes from data that would otherwise go unused.
Beyond the democratization and decision-making advantages, data fabric solutions are also proving essential to enterprise AI workflows. According to 2024 studies from the IBM IBV, 67% of CFOs say their C-suite has the data necessary to quickly capitalize on new technologies. But only 29% of tech leaders strongly agree their data has the necessary quality, accessibility and security to efficiently scale generative AI.
With a data fabric, organizations can more easily build a trusted data infrastructure for data delivery to their AI systems—with governance and privacy requirements automatically applied.
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Data fabric architectures leverage data catalogs, which are detailed libraries of data assets. These catalogs employ active metadata (which uses knowledge graphs, semantics and AI) to organize data assets in real time so that users can quickly and easily find the right data for their use cases. This metadata also provides a common business understanding of different data through taxonomies, ownership and activity information, related assets and more.
In a data fabric, the data integration process unifies data from disparate data sources, transforms it into a consistent structure and makes it accessible for data analytics and decision-making. This connection occurs through various integration styles, such as batch processing, real-time data integration and change data capture (CDC). Smart integration processes can maximize performance while minimizing storage costs.
A data fabric provides a unified way to create and enforce data governance and data security policies at scale. For instance, data access controls can be easily and automatically linked to sensitive data through metadata, such as user groups or data classifications. Through this trusted and protected business-ready data, data fabrics can help organizations operationalize AI.
A data fabrics acts as a self-service marketplace for data consumption. Through key governance capabilities—such as data profiling and metadata management—it empowers data engineers, data scientists and business users to quickly discover, access and collaborate on high-quality data. Users can search for data assets, tag and annotate them, and add comments. As a result, dependency on the IT department is significantly reduced.
Data fabrics also include end-to-end management throughout the data fabric lifecycle. By leveraging machine learning operations (MLOps) and AI, this approach delivers a unified experience for composing, building, testing, deploying, optimizing and monitoring the various components of a data fabric architecture—such as data pipelines.
A data mesh is a decentralized data architecture that organizes data by a specific business domain (for example, marketing, sales or customer service) to provide more ownership to the producers of a given dataset.
Data fabrics coexist with data meshes, and often enhance their functionality. They can automate key components of a data mesh such as creating data products and enforcing global governance.
Data lakehouses emerged to address the flaws of traditional data management platforms. They combine the flexible data storage capabilities of data lakes with the high-performance analytics of data warehouses.
Data fabrics can be considered the next stage in the evolution of data lakehouses and other data platforms. Organizations use them to simplify data management and improve access to lakehouse data. They help foster data sharing, automate data integration and governance, and support self-service data consumption—capabilities that storage repositories alone cannot provide.
Unlike individual data storage systems, data fabrics can create fluidity across data environments, counteracting the problem of data gravity—the idea that data becomes more difficult to move as increasing volumes of new data arrive. A data fabric removes the technological complexities required for data movement, transformation and integration, making all data available across the enterprise.
But how does a data fabric achieve this?
Data fabrics use an array of data services. To understand how it works, it's helpful to explore three foundational components: data virtualization, federated active metadata and machine learning.
Data virtualization makes data accessible without physically moving it. Instead of using traditional ETL (extract, transform, load) processes, a data virtualization tool connects directly to different sources, integrating only the metadata required. It then creates a virtual data layer that enables users to search and access data in real time, as if it were in a centralized repository.
Federated active metadata makes data more discoverable and usable. Unlike passive metadata, which is static and manually curated, federated active metadata uses semantic knowledge graphs and AI/ML technologies to continuously analyze metadata, detect patterns and unify data across diverse systems and formats.
These systems can automatically tag, profile and classify data. They can also trigger alerts or actions based on metadata changes, making data ecosystems more resilient and self-managing.
Machine learning automates critical processes within a data fabric, making it an advanced and intelligent data architecture. ML can be used to automatically enforce governance policies, generate real-time insights, detect security vulnerabilities, track data lineage, correct data quality issues and more.
While data fabric architectures vary by business needs, they share common features. According to Forrester’s Enterprise Data Fabric Enables DataOps report, a data fabric typically consists of six fundamental components:2
In addition to improving overall data management and access, data fabrics also offer the following business benefits:
Automating data governance, integration and other data services across multiple platforms streamlines data management and analysis. By reducing bottlenecks, businesses can boost productivity, enabling business users to make faster decisions and easing the workloads of technical teams.
Additionally, intelligent integration capabilities can help optimize performance while minimizing storage and costs.
Data fabric architectures facilitate self-service apps, broadening data access beyond technical teams. They give users a unified view of organizational data, creating connections regardless of where the data resides or how siloed it had previously been.
Accessible, visible data makes data cataloging and governance enforcement much easier. Broadened data access also often results in more governance guardrails and data security approaches, such as data masking and encryption for sensitive data.
Data fabric architectures are modular and built to scale. They can scale both horizontally (to accommodate ever-growing data volumes) and vertically (to enhance processes and performance).
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1 “The State of Dark Data,” Splunk, 2019
2 “The Forrester Wave™: Enterprise Data Fabric, Q2 2022: The 15 Providers That Matter Most and How They Stack Up,” Forrester, 2020.