What Is a Data Fabric?

A data fabric is a framework that connects and integrates data across platforms and environments to provide a unified, consistent view of enterprise data.

It weaves together disparate sources—whether on-premises, in the cloud, or in hybrid environments—into a single logical layer that simplifies access, sharing, and governance. By managing data at scale, a data fabric streamlines integration processes and ensures that trusted, real-time data is delivered to both consumers and applications. This unified approach is especially valuable for organizations with multiple systems, diverse data sources, or geographically distributed operations.

The adaptability of a data fabric comes from its use of metadata, automation, and governance to intelligently integrate data wherever it resides. By breaking down silos and reducing complexity, it accelerates analytics, enhances data quality, and supports advanced initiatives such as AI and machine learning. In doing so, a data fabric provides enterprises with a single, reliable source of truth that enables smarter decisions and drives innovation.

A data fabric is a framework that connects and integrates data across platforms and environments to provide a unified, consistent view of enterprise data.

It weaves together disparate sources, whether on-premises, in the cloud, or in hybrid environments, into a single logical layer that simplifies access, sharing, and governance. By managing data at scale, a data fabric streamlines integration processes and provides trusted, real-time data to both consumers and applications. This unified approach is especially valuable for organizations with multiple systems, diverse data sources, or geographically distributed operations.

The adaptability of a data fabric comes from its use of metadata, automation, and governance to intelligently integrate data wherever it resides. By breaking down silos and reducing complexity, it accelerates analytics, enhances data quality, and supports advanced initiatives such as AI and machine learning. In doing so, a data fabric provides enterprises with a single, reliable source of truth that enables smarter decisions and drives innovation.

Key takeaways

• A data fabric integrates data across on-premises, cloud, and hybrid systems using APIs and metadata.

• It uses metadata and automation to streamline integration and maintain consistency.

• The approach simplifies access, reduces data duplication, and speeds analytics.

• A well-designed fabric helps organizations scale data management and support AI-driven insights.

Then and now: What drives the need for data fabric?

Teams still struggle to access reliable data fast enough to make decisions. Data is scattered across systems, slowing innovation and adding complexity. Meanwhile, global data creation continues to explode, and IDC now projects the datasphere will grow to 221 zettabytes by 2026.

Originally, the idea of data fabric arose as a solution to the challenges facing traditional data management systems in siloed environments. As organizations generated more data and ecosystems grew more complex, they needed a more scalable approach. 

Why use a data fabric? Who is it for?

Here are some key reasons why organizations should consider implementing a data fabric:

• Data integration: Data fabric simplifies the integration of diverse data sources, including structured, semi-structured, and unstructured data from on-premises and cloud-based systems. This enables organizations to create a unified view of their data assets.

• Data accessibility: By creating a seamless access layer, data fabric makes data available across the organization, regardless of location or format. Consistent governance, policy-driven controls, and lineage visibility, rather than the data fabric itself, enable true democratization and informed decisions.

• Data governance: Data fabric embeds data lineage and metadata to strengthen governance frameworks. It also enhances accuracy, security, and compliance. However, it complements rather than replaces a comprehensive governance methodology.

• Scalability: Built to handle large volumes of data, data fabric easily scales to meet the growing needs of modern enterprises. It also manages data across hybrid and multi-cloud environments, giving teams flexibility and scalability.

Data fabric is particularly beneficial for the following groups:

• Data analysts: Data fabric provides analysts with easy access to diverse data sources. It also enables them to perform comprehensive analyses and derive actionable insights.

• Data scientists: By offering a unified data environment, data fabric simplifies the data preparation and experimentation processes. The technology lets data scientists focus on developing and deploying machine learning models.

• Data engineers: Data fabric streamlines integration and transformation workflows, reducing the effort to manage data pipelines. But it does not remove the work entirely.

• Business leaders: For decision-makers, data fabric delivers real-time insights and a holistic view of organizational data to facilitate informed, strategic decisions.

How a data catalog supports data fabric

A data catalog is a key part of a data fabric architecture. It provides capabilities that make data easier to manage and use. In a data fabric, a data catalog supports operations in several important ways:

• Data discovery: A data catalog indexes and organizes assets so users can find and access relevant data. In a data fabric, the catalog also supports the semantic layer, connecting meaning with access across multiple sources.

• Metadata management: Data catalogs capture and manage metadata, including data lineage, data quality, and usage statistics. This metadata provides valuable context for understanding and using data within the fabric. 

• Metadata: A data fabric uses metadata to create abstraction layers, connect to data sources, retrieve data, and power AI-driven processes. This metadata can be static or dynamic:

• Static metadata: Teams create this metadata during design time, maintaining it as documentation for things like data schema and business definitions.

• Dynamic metadata: Active metadata that provides changing insights into parameters like data quality and frequency of access.

• Data governance: With built-in governance features, data catalogs can handle data responsibly and comply with regulatory standards. They also allow teams to track data usage and apply access controls, a vital component for maintaining data integrity.

• Collaboration: Data catalogs facilitate collaboration by allowing users to annotate and share data assets. They also promote knowledge sharing and reuse across the organization.

Integrating a data catalog with a data fabric helps organizations unlock the full potential of their data. Together, they help teams drive smarter decisions and power advanced analytics.

Data mesh vs. data fabric: Key differences

While data mesh and data fabric are both approaches to modern data management, they differ in their underlying philosophies and implementations. Understanding these differences is crucial for selecting the right strategy for your organization.

Key principles of data mesh:

• Decentralized ownership: Data mesh advocates for a decentralized approach where domain teams own and manage their data products. This promotes domain-specific expertise and accountability.

• Domain-driven design: This configuration organizes data around business domains,  letting teams build data products that actually match business needs.

• Self-service infrastructure: Data mesh emphasizes self-service capabilities. It allows teams to build and manage their data products independently.

Key principles of data fabric:

• Centralized management: Data fabric provides a centralized data management layer that integrates and governs data across the organization. This ensures consistency and compliance.

• Unified data environment: The data fabric creates a unified data environment that simplifies accessing and analyzing data from various sources.

• Scalability and flexibility: The data fabric is built to scale and handles large volumes of data across hybrid and multi-cloud environments.

While both approaches aim to make data more usable, they take very different paths to get there. Choosing between a data mesh and a data fabric depends on your organization’s priorities and how you want teams to engage with data.

What are the key layers of a data fabric framework? 

 A data fabric has several layers that work together to make data easier to use and manage. Each layer plays a distinct role, but together they create a unified system for handling data across the organization:

• Ingestion layer: Data from on-premises systems and cloud storage, brought in through batch or real-time streams

• Processing layer: Data cleaning, transformation, and preparation for analysis

• Orchestration layer: Workflow coordination, dependency management, and error handling

• Discovery layer: Tools for finding, exploring, and understanding available data

• Data access layer: Secure methods for querying and retrieving information

• Data management layer: Oversight of data quality, governance, and compliance across the ecosystem

Together, these layers provide a framework that makes data easier to find and use for decision-making.

How do you set up a data fabric?

Implementing a data fabric involves several key steps to ensure a successful deployment. Here’s a high-level overview of the process:

• Assess data needs: Begin by assessing your organization’s data needs, including data sources and data governance policies. This understanding provides the foundation for designing a fabric that meets real business needs.

• Select technologies: Next, choose the appropriate technologies and tools to support your data fabric architecture. This may include data integration platforms, data catalogs, and cloud services. Then, think about how these technologies will work together to simplify access and management.

• Integrate data: Make sure to integrate data from various sources into the data fabric. A strong data integration process harmonizes it and makes it more accessible. It also builds trust in your data and ensures teams can use it for analysis. 

• Implement data governance: When done right, strong data governance can help teams improve data quality and keep metadata organized. Following these policies also ensures everyone stays compliant and reduces the risk of errors.

• Enable data access: It’s essential to set up data access mechanisms so users can easily find and use data within the fabric. This ensures secure access and intuitive search, letting teams get what they need without bottlenecks.

• Monitor and optimize: Keep a close eye on the performance of your data fabric and adjust it as needed to maintain effectiveness. Regularly tracking data usage and assessing quality helps ensure reliable outcomes. Meanwhile, refining workflows keeps the system running smoothly and efficiently.

With these steps in place, a data fabric can grow with your business, evolving to support new data sources and use cases over time.

How a data fabric supports AI (and vice versa) 

The synergy between data fabric and AI is a game-changer for organizations looking to harness the full potential of their data.

How data fabric supports AI

Data availability: Data fabric makes sure that AI models can access high-quality data from multiple sources. This capability makes predictions more accurate and reliable. 

Data integration: By integrating data from multiple systems, data fabric provides a comprehensive view of data. This makes it easier to build more sophisticated AI models and analyses.

Real-time data: Data fabric facilitates real-time data processing, which allows AI models to generate insights and act based on the latest information.

How AI supports data fabric

Automated data management: AI can automate various data management tasks within the data fabric, such as data classification, anomaly detection, and data cleansing.

Enhanced data governance: AI-driven tools can enhance data governance by automatically identifying sensitive data, monitoring data usage, and enforcing compliance policies.

Improved data insights: AI-powered analytics can uncover hidden patterns and trends within the data fabric to provide valuable insights that drive business decisions.

3 governance-as-code best practices for data fabric

Governance as code means expressing data governance policies such as access controls, data quality rules, and compliance checks as executable code instead of manual procedures. This approach makes governance automated, testable, and consistent across systems.

Adopting this approach helps organizations enforce policies reliably across their data fabric, reduce manual effort, and keep governance aligned with modern, fast-moving data pipelines.

To get the most out of governance-as-code, organizations should follow a few best practices. The following three approaches help ensure consistent enforcement, reduce errors, and keep your data fabric running smoothly:

Defining governance policies as code  

Translate your data governance policies into code to ensure consistency and automatic enforcement. Doing so lets teams manage rules programmatically, which reduces errors and keeps compliance on track.

Embedding policy checks into CI/CD pipelines  

Add governance checks directly into your CI/CD workflows to automatically validate new pipelines and schema changes. These checks enforce data quality thresholds and access policies before deployment. They also catch issues early and keep your data environment reliable.

Managing versions and testing governance rules  

Use version control to track changes to governance rules, just like application code. Once changes are made, test new or updated policies in a safe environment to confirm they work as intended. This process improves transparency and accountability while reducing the risk of disruptions.

Benefits of implementing a data fabric with Alation

Implementing a data fabric with Alation brings several advantages that can significantly enhance your data management capabilities. Implementing a data fabric with Alation provides several key benefits, including:

• Enhanced data accessibility: Alation’s data catalog enables users to easily discover and access data across the fabric. This accessibility promotes data democratization and supports self-service analytics.

• Improved data governance: With robust governance features, Alation ensures that data within the fabric is accurate, secure, and compliant with regulatory requirements.

• Streamlined data Integration: Alation’s data fabric architecture simplifies data integration processes. It also reduces the time and effort teams spend connecting diverse data sources.

• Scalable and flexible: Alation built its data fabric to scale, so it easily handles large volumes of data across hybrid and multi-cloud environments. It can also adapt to keep up with changing business needs.

Alation’s data fabric lays the foundation for real business impact. When teams connect and organize their data, they can turn insights into action, just like Kroger has done.

Data fabric use case: How Kroger unlocks value 

Kroger, one of the largest grocery retailers in the United States, has effectively implemented a data fabric to unlock significant value from its extensive data assets. Facing the challenge of siloed data across departments and locations, Kroger turned to a data fabric and data mesh to create a unified data environment.

By adopting a data fabric, Kroger was able to integrate disparate data sources, including customer transaction data, supply chain information, and inventory levels. This integration provided a holistic view of its operations and enabled more informed decision-making across the organization.

One of the key achievements of Kroger's data fabric implementation is enhanced customer insights. With a unified data environment, Kroger gained a deeper understanding of customer preferences and behaviors. This allowed for more personalized marketing strategies and improved customer experiences, ultimately driving customer loyalty and sales.

The company’s operational efficiency also saw significant improvements. Its data fabric streamlined data integration and management processes, reducing the time and effort necessary to handle data workflows. This efficiency translated into cost savings and more agile operations, allowing Kroger to respond swiftly to market changes and customer demands.

Build a metadata-driven data fabric strategy  

A data fabric provides a unified, scalable approach to managing complex modern data environments. In practice, it brings together diverse data sources and boosts governance, helping teams get more value from their data. For analysts and engineers, it simplifies integration and management, while business leaders gain real-time insights and a complete view of their data to make informed decisions.

Integrating a data catalog improves data discovery and strengthens governance, while also enabling AI to automate processes for deeper insights. Data fabric builds on this by providing centralized management and a unified environment, which is ideal for organizations seeking consistency and control.

Implementing a data fabric begins with assessing needs and selecting appropriate technologies. It then involves integrating data, establishing governance, and continuously monitoring performance to ensure the system remains adaptable. Meshy data fabrics go further by combining centralized management with decentralized access. This approach gives teams the flexibility to work independently while maintaining overall oversight.

Curious to learn how a meshy data fabric can help your organization? Download this step-by-step guide to learn how to create one today. Or book a demo to see for yourself.