Data fabric is an innovative approach to data architecture, and simplifies data management. At its core, data fabric is built on the principle of unification. This standardization serves two purposes: It creates a single-entry point for data consumers, and it enables seamless access to information, regardless of where that data is stored, computed, or administered.

These outcomes happen through data virtualization, a key technology that creates a single, abstract layer. This component sits on top of various database systems and their underlying processes. 

To connect all this information through virtualization, a data fabric relies heavily on metadata, which is information that provides context and understanding. Through the intelligent use of metadata, a data fabric can perform self-optimization and integration tasks, reducing the need for manual intervention.

Artificial intelligence (AI) and machine learning (ML) enhance these capabilities, and their effectiveness is fundamentally tied to the quality of metadata management. Moreover, metadata management ensures that the data fabric provides the right information at the right time and gives business users confidence in finding what they need.

Data Fabric Defined

While the concept of data fabric centers on unification, its full definition encompasses much more. These include ideas of versatility, scalability, high performance in the entire data ecosystem, and ease of use.

William McKnight, a data management guru who advises many of the world’s best-known organizations, observes that companies gravitate toward a data fabric because of its portability – the ability to move applications easily between different environments. This capability is crucial as corporations update their technology stacks.

Gartner, a leading research and advisory company, emphasizes that a data fabric creates a scalable solution by supporting a “combination of different integration styles,” including data integration pipelines, services, and semantics.

Additionally, a data fabric bundles a sophisticated toolset to automate and improve entire business processes, from end-to-end. These services and architectures deliver “reliable capabilities across data environments.”

Is a Data Mesh the Same as a Data Fabric?

An understanding the definition of data fabric is necessary to distinguish it from other related data architecture concepts. One term often arising in discussions alongside data fabric is “data mesh.”

Given their similar goals of improving data accessibility and management, these two concepts are frequently confused or conflated with each other. However, they represent distinct approaches.

A data mesh is an infrastructure pattern that takes a microservices approach. Here, domains own their data, create a product, and are responsible for ensuring other business departments in the enterprise can use this product.

On the other hand, a data fabric takes a more overarching and technical strategy. It connects all the data elements in the data ecosystem, which may include a data mesh. Think of a cloth tote bag with mesh pockets. The fabric composes the entire tote bag.

Data Fabric’s Components

Many components, such as data mesh, data lakes, or databases may make up a data fabric. Some constituents stand out for their roles in centralizing data access:

• Data Virtualization: As mentioned in the intro above, data virtualization creates a unified, abstract layer across various data sources, enabling seamless access. Consequently, data virtualization significantly reduces the time and resource requirements needed to physically move data among different sources, making it a key enabler of the data fabric architecture.
• Metadata Management: In a data fabric, this component goes beyond basic metadata administration providing advanced support for AI and ML. It focuses on the efficient reuse of metadata through data governance. Good metadata management ensures that the metadata that flows through the fabric is accurate, consistent, and securely accessible.
• Data Catalog: The data catalog serves as a critical navigational tool. It inventories and organizes metadata about all connected data assets within the fabric.
• AI/ML: As mentioned in the intro above, AI and ML underlie automation and integration processes across multiple fabric elements, including data catalogs. With AI, data fabrics can find and integrate new data sources, recognize new schema, promote self-service consolidation, and correct any schema drifts, where newly ingested data no longer aligns.
• Knowledge Graphs: Knowledge graphs represent a collection of real-world concepts and their associations. These tools train ML algorithms and help generative AI interpret metadata patterns to make recommendations and align with business meaning.

These factors allow data fabric to provide many benefits. 

Benefits of Data Fabric 

Data fabric offers many benefits to organizations, including:

• Enabling data access and collaboration among non-technical and technical professionals
• Detecting fraud
• Providing preventative maintenance of the whole system
• Profiling customers for personalized services
• Performing risk-modeling
• Optimizing data flow through the continuous use and reuse of metadata

These benefits simplify data access, address stakeholder needs, and more efficiently meet business requirements. For more details, see the article Data Fabric Tools: Benefits and Features.

Challenges of Data Fabric

While data fabric offers many advantages, organizations may face several challenges during implementation. Some common ones include:

• Handling the complexity of a vast mosaic of different data systems, including legacy ones
• Getting stakeholder buy-in by demonstrating clear business value
• Managing conflicting views about data unification requirements based on worries about loss of data management control 

These obstacles can stop organizations from realizing the full potential of a data fabric implementation. To dive deeper into these hurdles and ways to address them, refer to Implementing Data Fabric: 7 Key Steps.

Data Fabric Use Cases

When organizations overcome data fabric challenges, they empower users to gain striking insights. A data fabric has this advantage by giving businesses quick access to the right data at the right time, as demonstrated below:

• Quest Diagnostics faced distributed data across on-premises and cloud environments and managed by various software platforms. The company, with Promethium, implemented a data fabric to get a consistent view of data across these different systems.
• Ducati, an Italian motorcycle manufacturing company, has a large amount of data that drives innovative bike creation. It partnered with NetApp to release a data fabric framework to gather and benefit from the data collected by sensors on each of its motorcycles.
• Sainsbury, a retail marketplace, has information about various brands spread across dozens of disparate systems. After establishing a trusted data fabric, Sainsbury leveraged over 30 custom analytics applications to drive everyday decision-making on the store floor and across corporate functions. 
• Syracuse needed a single, unified data platform to address various operational and research needs. It invested in data fabric to connect private and public cloud data centers, which improved data access and availability.
• BMC helps companies across the globe deliver and consume digital services. It transformed a decentralized and manual ledger and accounting process through a data fabric. Working with Informatica, BMC gained better visibility into actual and projected cash flows.

These examples provide only the tip of the iceberg of what data fabric can provide and its promises for the future.

What’s Next? 

More organizations will turn to data fabric as a critical data management strategy as the types and number of data sources continue to grow. Consequently, companies will prioritize virtualization and metadata management implementations needed to support a data fabric infrastructure.

With these components in place, companies will benefit from future improvements, including:

• Advanced AI and ML Integration: ML will cluster related data sets together and integrate new data sources into the business’s data ecosystem. As a result, organizations will better adjust to changes in the business context by making quicker, more relevant decisions.
• Enhanced Real-Time Processing: Advances in real-time processing will spill over to immediate insights, e.g., what customers are saying now about an organization. Additionally, companies will perform more efficient data operations in the data fabric. Consequently, companies will make decisions and counter cybersecurity issues more quickly.
• Better Self-Service Functionality: Technical improvements in data cataloging will give business users more efficient and effective self-service functionality. Through generative AI dialogs, businesspeople will get recommendations on how to optimize their data views and usage. This will make data more available to nontechnical professionals.
• More Sophisticated Security: Improvements in privacy and security promise a more comprehensive protection of the data fabric. Advances in automatic policy enforcement and encryption control audits will better prevent unauthorized access. Preventing and identifying a cyber-incident will become easier.
• Expanded Multi-cloud and Hybrid Cloud Support: Cloud computing is expected to gain traction with improvements in multi-cloud and hybrid cloud support. This advantage will facilitate data unification across the data fabric architecture. Businesses will be able to exchange data sets more efficiently internally and among their external partners.

These exciting improvements in how data fabric functions will further the development of other emerging technologies.

• The Internet of Things (IoT): Data fabric ensures efficient insights from the Internet of Things (IoT), a network of devices and sensors. It handles a vast amount of data and minimizes latency in IoT data production by automatically selecting the most appropriate processes and scales. These advantages enable manufacturers to innovate and create more useful, better-made products.
• AI and Generative AI Projects: Data fabric provides ML with good flexibility and data delivery. This approach reduces data preparation time and allows AI to learn more efficiently. Consequently, AI systems can work with higher data quality, leading to more accurate predictions and better alignment with business needs.
• Big Data Analytics: Data fabric provides unification, integration, and real-time processing for big data. It offers immediate access to data through metadata-driven capabilities. This cost-effective approach allows more organizations to benefit from big data analytics.
• Advanced Security and Privacy: Data fabric combines virtualization with metadata management for comprehensive security coverage. It expedites permission handling and automates security policies through standardization. These advantages help companies continue to reduce risks, while effectively managing data governance policies.
• Multi-Cloud and Hybrid Cloud Support: Data fabric architectures unify cloud elements in multi-cloud and hybrid environments. This integration increases flexibility in choosing cloud capabilities. As a result, companies gain more freedom in implementing their data architecture across different cloud platforms.

As organizations discover new technologies in the future, they will continue to benefit from the data fabric’s advantage of unification. Data virtualization and metadata management will become more refined to take us into the next generation.