A data fabric is a data management design that uses metadata to automate integration work across systems you already run. It can reach data in place and consolidate only where that helps, so existing warehouses and lakes stay put.
- Metadata is the mechanic. A data fabric turns metadata that sits unused into metadata that identifies actions across the systems sharing the same data.
- You assemble one from several components. Gartner states that no single vendor currently delivers all of them.
- AI projects are where weak data management surfaces. In February 2025, Gartner predicted organizations would abandon 60% of AI projects through 2026 that lacked AI-ready data behind them.
- The build starts with augmenting your data catalog, which is step one in Gartner's sequence.
Your data sits in more systems than anyone can track, and the work to connect it keeps growing. A data fabric uses metadata to automate that connecting. This guide covers what a data fabric is, how it works, and how it differs from a data mesh.
What Is Data Fabric?
Data fabric is a data management and data integration design concept that uses metadata to automate data management tasks and remove manual integration work. Gartner frames its goal as supporting data access across the business through integration that is reusable and, in places, automated. The design connects data where it already sits and consolidates only where consolidation helps, so your existing data lakes and warehouses stay in place.
A data fabric also gives you access to all your data when it’s needed. When you have this type of arrangement, it reduces the need for redundant processes because all the data is readily available without having to go through many distinct steps.
Aside from providing access to real-time updates, the fabric also allows for controlled distribution. You can grant permissions from anywhere and at any time. So a remote team gets the same controlled view of the data as a team in the office.
How Does Data Fabric Work?
A data fabric provides an organization with the ability to collect and analyze massive amounts of structured or unstructured data from multiple sources. It takes in metadata from the systems and users that already touch your data, analyzes it, and returns alerts and recommendations on how that data could be better organized and used. Gartner describes this as the core capability of the design.
Users have full control over their information, as it’s only accessible by those with specific access rights. Data fabrics are composable. You assemble one from several technical components rather than buying it as a product, and Gartner states that no single vendor currently delivers all data fabric components. Expect to combine tools from more than one vendor.
When Should You Use a Data Fabric?
You use a data fabric when integration work by hand has become the bottleneck, and when the AI work downstream depends on data you cannot yet trust. A Gartner survey of 248 data management leaders found that 63% of organizations either lack the right data management practices for AI or are unsure whether they have them, and Gartner predicts organizations will abandon 60% of AI projects through 2026 that run on data which is not AI-ready.
That access has to reach every source, whether it is an Internet of Things (IoT) sensor or a traditional enterprise application.
And while no one wants to manage data silos that are rife with incompatibility and redundancy, it’s challenging to connect the causes of these challenges and the solutions through traditional data integration. Businesses need one approach to data that works across software types and storage locations.
Data needs to be accessible to users who need it, regardless of department or information technology (IT) silo. Businesses need a secure, efficient, unified environment that accesses and transforms information, and data fabric technology provides that.
What Are the Benefits of a Data Fabric?
Data fabrics can be beneficial for businesses in a variety of ways, particularly by improving the speed and efficiency with which they can access important insights from their data. Here are some of the key benefits of a data fabric:
1. Data Integration
The best way to get the most out of your big data is to integrate it with your existing enterprise information systems. When you’re using multiple applications and software to run your business, having a data fabric can help you integrate all of this information in order to get a holistic view of your enterprise. With the data fabric, data is no longer fragmented across files, servers, and departments.
This technology provides a single platform that unifies data, allowing users one place to store, manage, and access their data from anywhere with internet access. And this helps you make more informed decisions based on the data you have at hand. Once you have created an integrated system in which all relevant data is updated in real time, you can perform analytics on them any time without further preparation.
2. Eliminating Data Silos
While data silos have historically been the way organizations worked with data, inside a cluster or department, the fact is that they hamper productivity, even though they are quite beneficial in operational cases. This is because silos operate as independent entities that prevent organizations from accessing all of their enterprise data at once. This increases the time it takes to complete simple tasks like finding the right contact information, or comparing how a certain metric looks between months.
The data fabric can help companies overcome challenges associated with data silos. By using a data-centric approach, big data can be more easily and timely accessible across organizations. Data fabric supports a wide range of services by building on open-source technologies. This means a user can access various data sources from anywhere in the world.
3. Risk Mitigation
Risk mitigation is a major benefit of a data fabric. This was previously a challenge, as extract, transfer, and load (ETL) processing had to be managed actively. However, once the infrastructure has been put into place and configured to suit operational needs, the data fabric will handle the ETL process seamlessly. This prevents large-scale risk with data migration and safety measures in place in case of any incidents that may affect the data pipeline.
A data fabric addresses risks by incorporating real-time analytics to identify potential issues and make adjustments as needed. The agile model allows new data to be fed into the system without the worry of corrupting data assets. The platform communicates any forthcoming delays with both product development and customer experience. This keeps all departments informed of operational issues. The data architecture can also monitor compliance with regulatory guidelines.
4. Advanced Search Time
With a unified interface, a data fabric simplifies searching for information because it allows you to search across multiple databases at once. With this function, your team can save time by conducting combined searches instead of searching for specific information within each database separately.
If you run a business from many locations, a hybrid cloud can allow you to access all of your data from any of your locations. This feature is helpful for companies that store sensitive or proprietary data across multiple locations.
5. Scalability
Modern enterprises require large amounts of storage space as they grow and expand into new markets. Since a flexible cloud-based system makes it easy to scale up or down as needed, a data fabric will allow you to grow your business without having to worry about outgrowing your infrastructure. A data fabric must be able to expand with your business. If you are considering adding new applications or connecting with new partners, you should have enough bandwidth and computing power to do so easily. A data fabric maintains its integrity as databases expand and can deliver high-quality performance as its volume increases.
6. Improved Data Governance and Compliance
Data governance is an organization’s strategy for making sure its data is useful, reliable, and protected in the right ways. In industries such as healthcare and financial services where privacy regulations are strict, organizations need to show their ability to meet regulatory requirements when audited. Data fabrics can make compliance easier by centralizing data collection, storage, and management in one place so that administrators can better understand how data flows throughout the enterprise and where they might need to put extra controls in place.
7. Improved Data Security and Privacy
Data security and privacy are also two major concerns for most organizations. With data fabric, an organization has complete control over who has access and what they have access to within the fabric, making it easier for them to stay compliant with data regulations. This is important because each organization will have different security requirements based on the importance of their data and how they want data secured.
8. Reduced Costs
Many companies are already leveraging this technology as it offers them flexibility in how they manage their information and compliance with various regulations, while also reducing costs associated with storage and management of data. With data fabric, companies can reduce the cost of infrastructure by simplifying infrastructure management, lowering the cost of storage, improving utilization rates, and more.
Where Should You Start With a Data Fabric?
A data fabric gives you one governed way to reach data that stays spread across your systems. The design is still maturing, and Gartner notes that no vendor delivers every component of it today.
Start with a data catalog and the metadata it collects. Gartner puts that first in the build sequence, and it is what makes the later automation steps possible.
MEV’s Expertise and Data Fabric
MEV is a software company that specializes in a variety of technology services, including data fabric-related services. We offer consultation services from industry experts as well as partnership opportunities. We provide data fabric implementation services, which includes identifying key sources of metadata, building a data model MVP, aligning your data to the model MVP, setting up consumer applications, and evolving your data fabric over time. We also provide ongoing support and maintenance and proactive fixes to ensure the architecture is running smoothly at all times.
If you want a review of your current architecture or a partner for data fabric implementation, talk to our team.
This page is for data and engineering leaders who are weighing a data fabric against the integration setup they already run. It defines the architecture, then works through the eight benefits that decide whether the model earns its cost in a given environment. The closing section covers what a data fabric implementation involves at MEV, starting with metadata source identification and a data model MVP, so you can size the work before committing budget.
