Data Fabric

Data Fabric: What it is and How it Works

Organizations often struggle to manage and integrate vast amounts of data across multiple systems. Data fabric offers a unified approach, enabling seamless access, intelligent automation, and enhanced governance across hybrid and multi-cloud environments.

This guide explores the core principles, benefits, and implementation strategies of data fabric, helping businesses unlock the full potential of their data.

What is a Data Fabric?

A data fabric is an architectural approach that enables seamless, integrated data management across diverse environments, including on-premises, cloud, and hybrid systems. It leverages AI, metadata-driven automation, and intelligent data orchestration to provide real-time access to data, ensuring consistency, security, and efficiency.

Unlike traditional data integration methods, which rely on manual processes and rigid pipelines, data fabric creates a flexible, scalable ecosystem that connects disparate data sources. This allows organizations to access, analyze, and govern data seamlessly without the complexities of traditional data silos.

By enabling real-time data discovery, integration, and automation, data fabric enhances decision-making, supports advanced analytics, and improves overall business agility.

Data Fabric vs Data Mesh

Data fabric and data mesh both address data management challenges but take different approaches. A data fabric is a single virtual centralized system with centralized data ownership and stewardship. A data mesh uses a federated set of domain-specific data product services with stewardship and data ownership at the domain level. The data mesh is more of a peer-to-peer model with domains sharing data horizontally.

While data fabric focuses on automation and integration, data mesh prioritizes people and processes by distributing responsibility. Data fabric is ideal for organizations needing real-time, scalable data management, while data mesh works best for large enterprises with multiple domains. Many companies combine both approaches, using data fabric’s automation with data mesh’s decentralized structure to build a scalable, well-governed data ecosystem.

The Benefits of a Data Fabric

The primary benefits of a data fabric include the following:

• More data-driven decision-making and easy access to high-quality, trusted data
• Higher business agility to ready access to AI/ML insights
• A consistent user experience around the globe regardless of data format
• Secure access to data protected by firewalls, encryption, and strong authentication protocols
• Lower training and infrastructure costs due to more standardized integrations and services
• Futureproofed architecture for new projects to build upon
• More metadata, cataloging
• Better regulated data to support compliance and governance efforts
• Fewer point-to-point data integrations to support thanks to the integration bus approach that a data fabric provides
• Data becomes more of a utility as both professional data scientists, and casual data analysts can easily access necessary data and insights
• Knowledge graphs map relationships between data elements to make data discovery and exploration easier for technical and non-technical users
• The user-facing data consumption layer hides the complexity of accessing underlying data using APIs and SDKs from data consumers
• End-to-end security is achieved thanks to the transport layer applying source-to-user encryption

The Drawbacks of a Data Fabric

There are also some potential pitfalls of data fabric to consider. You’ll need to:

• Pick smaller systems to test your model or risk failure at an early stage.
• Include citizen analysts as data fabric testers to make it as usable as possible.
• Build in feedback mechanisms to keep the mesh updated with needed features or risk creating another stagnating data lake.
• Instrument existing systems to ensure you don’t waste energy migrating unused systems to the data fabric.

Why Use Data Fabric?

A data fabric is designed to make high-quality, secure data and insights available to any user, anywhere. It is a holistic approach for accessing all the available data assets in a business regardless of location.

This replaces traditional point-to-point data integrations with a consistent integration layer. Machine learning models, for example, need access to large amounts of data to train them, as more extensive data sets result in more accuracy. Metadata in the data fabric is connected by knowledge graphs which help connect related data sources.

The whole business can enjoy ubiquitous data access regardless of what hardware or cloud it runs on. Both structured and unstructured data are accessible with a consistent user experience.

An approach of this nature to data infrastructure is the answer to the escalating costs of maintaining a heterogeneous environment by bringing all storage and access services under one unified umbrella.

Getting Started With a Data Fabric Project

The first step in initiating a data fabric is to build a cross-functional team of data owners, consumers, and infrastructure professionals. Existing systems and integrations need to be cataloged. The most business-critical insights will need to be prioritized.

The user experience needs to be architected, designed, built, tested, and refined.

A cross-platform data management system needs to be selected. Ideally, one that can reside on-premise and in multiple clouds. The Actian Data platform is such a solution.

Storage needs to be easily accessible and elastic. This would be a good time to adopt block storage in the cloud which is more efficient and can be assigned elastic compute resources as user load increases without sacrificing performance.

Data replication technology maintains geographically local copies of critical data if network latency becomes a significant bottleneck.

Data Fabric FAQs

Check out the answers to some frequently asked questions about data fabric below.

What is data fabric architecture?

Data fabric architecture is a unified, intelligent data management framework that enables seamless access, integration, and governance across hybrid, multi-cloud, and on-premises environments. It leverages AI, metadata-driven automation, and data orchestration to connect disparate data sources in real time, ensuring consistency, security, and scalability. Unlike traditional data architectures, data fabric dynamically adapts to business needs, providing a flexible, self-service approach to data access and analytics. This helps organizations break down silos, enhance decision-making, and improve operational efficiency.

Who uses data fabric?

Data fabric is used by large enterprises, data-driven organizations, and IT teams that need to integrate and manage data across hybrid and multi-cloud environments. Industries like finance, healthcare, retail, and manufacturing leverage data fabric for real-time analytics, automation, and governance. It is particularly beneficial for data engineers, analysts, and business leaders who require seamless, secure, and scalable data access.

What is the difference between data warehouse and data fabric?

A data warehouse is a centralized repository designed for storing and analyzing structured data from multiple sources, using predefined schemas for batch processing and reporting. In contrast, data fabric is a dynamic, AI-driven architecture that connects, integrates, and manages both structured and unstructured data across hybrid, multi-cloud, and on-premises environments in real time. While a data warehouse is rigid and requires ETL (Extract, Transform, Load) processes, data fabric provides flexible, automated data access and governance across distributed systems.

What problem does data fabric solve?

Data fabric solves the problem of data fragmentation by providing a unified, automated platform for integrating, managing, and accessing data across disparate systems and environments, such as on-premises, cloud, and hybrid infrastructures. It eliminates the challenges of data silos, ensuring real-time, consistent data across organizations while automating governance, security, and compliance.

The Actian Data Platform

The Actian Data Platform can underpin a data fabric implementation by supporting data stored and analyzed on-premise or multiple cloud platforms. Actian eases integration with its built-in connectors to hundreds of prebuilt connectors to sources, including NetSuite, Salesforce and ServiceNow. The Actian Data Platform uses a vectorized columnar database that outperforms alternatives by 7.9x to deliver answers faster.