What is Data Fabric Architecture?

Data is the lifeblood of many organizations. The data revolution has changed how we do business and has increased our knowledge of making better decisions. However, there are still significant challenges in building an architecture that can handle all this data effectively. This article will discuss what a data fabric architecture is, where it came from, and why you would want to implement one for your organization.

Table of Contents

  1. What Is the Data Fabric Architecture and What Does It Do?
  2. The Importance of a Data Fabric
  3. Components of a Data Fabric Architecture
  4. Why Do You Need to Use Data Fabric Architecture Today?

What Is the Data Fabric Architecture and What Does It Do?

A data fabric is an architecture created to solve the problems of having data distributed across many databases, applications, and clouds. It provides a unifying layer for consolidating all types of enterprise data into one virtual platform so it can be used throughout your business in real time. This allows you to drive better decision-making by providing insight into how well different parts of your organization are performing with integrated information about customers, products, process performance, etc. The result is increased revenue through improved efficiency and reduced costs.

The Importance of a Data Fabric

Data is at the core of many businesses. Finding a way to effectively and efficiently store, manage, and analyze this data gives organizations an edge over their competition. The ability to rapidly access and process data has become essential for companies looking to grow.

Components of a Data Fabric Architecture

Every implementation of a data fabric architecture will vary. However, there are some key components that should be considered. These components may include:

Data Virtualization

This is the foundation of any data fabric architecture because it provides an abstraction layer for accessing all enterprise data sources as one virtualized pool (like an on-demand database). The result is the ability to transform raw data into real-time analytics without changing underlying source systems or query languages. Users can also use this technology to build custom reports and dashboards like charts, graphs, etc. Data virtualization allows users to access both structured and unstructured information in their preferred format, making analysis faster and easier than ever before.


This component ensures consistent policies across your entire platform by automating the data fabric's provisioning, management, and governance. It also provides a single point for enforcement that can easily be integrated into existing systems to ensure security standards are being met at all times.

Data Governance

Data governance is an essential component because it allows users to implement policies across their entire platform while simultaneously enforcing them in real-time. By having this type of control over your infrastructure, you will have better visibility into who has access to what information and how they are accessing it. This ensures compliance with regulations like GDPR or HIPAA so organizations can continue working efficiently without worrying about violating strict industry mandates. Data governance tools include auditing, archiving metadata tagging, etc.

Data Hub

The data hub acts as an integration layer that allows you to collect and store data from anywhere in your organization. It then cleanses, enriches, and makes it available for analysis across different business units or departments through connectors that provide access via web services APIs. This hub offers a single view of all enterprise-wide data regardless of its source so users can get consolidated analytics on their business performance in real time without having to move between multiple disconnected systems.

Fabric Controller

The fabric controller manages information flow throughout your entire ecosystem by providing governance capabilities such as authentication/authorization and security features like rest and end-to-end encryption. It also offers dynamic routing capabilities for data movement and distribution to optimize performance based on the needs of your business.

Data Quality

The data quality layer ensures all information you receive has integrity by using algorithms to detect, alert and correct inconsistencies in real-time before they are passed into the fabric. This layer works with a rules engine that can be customized depending on how strict data quality should be within certain parts of your organization.


The analytics layer allows decision-makers to run complex queries against aggregated data sets from across all systems being analyzed within their ecosystem. The analytics provided here provide deep insights on things like customer behavior and operational performance metrics such as logistics processes or manufacturing KPIs so companies can make better-informed decisions about their business.

Why Do You Need to Use Data Fabric Architecture Today?

As the world becomes more interconnected with technology, the need for data management has increased exponentially. Data is now being created faster than ever before, and organizations are struggling to keep up. Proper data fabric architecture can help your business improve:

  • Business intelligence: You will be able to get a better understanding of your business performance.
  • Process efficiency: You can optimize processes across departments using information from different data sources.
  • Business agility: It allows you to respond faster and more accurately to market changes by accessing essential data in real-time.


Data fabric architecture provides the foundation for an effective data management strategy. By implementing this type of technology, you can manage your information storage, governance, and analysis processes in a way that makes it easier to access important information throughout your organization. If your business is looking to incorporate data fabric into your data infrastructure, contact Integrate.io for a 14-day demo


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Glossary of Terms

A guide to the nomenclature of data integration technology.