by Danielle Bingham | March 13, 2024

Data Fabric vs. Data Virtualization: Definitions, Use Cases & Key Differences

Managing the immense volumes of data that modern organizations accumulate is a constant challenge. The complexities of integrating, accessing, and analyzing that data are a continual source of frustration as they work to get the most value out of that data for strategic advantage.

Two innovative methodologies—data fabric and data virtualization—have gained prominence over the last several years, offering flexible solutions to these challenges. But what do these terms mean, and how do they apply to the challenges of data-heavy organizations?

In this article, we'll provide definitions of both methodologies, explain their uses, and pick apart the differences to help demystify these concepts and show how they can help organizations navigate the complex landscape of modern data management.

Understanding data fabric and data virtualization

Data management strategies like data fabric and data virtualization are powerful tools that address today’s challenges of data diversity and accessibility in different ways. Each term reveals its essential meaning, but there is a lot more to it. Let’s go over the basics:

What is data fabric?

Data fabric is not just one thing; like cloth containing thousands of interwoven threads, it is a unified architectural approach that provides data access to be shared across an organization. It combines data management and data integration tools to access data (the threads) from multiple data sources, regardless of where they are—on-premises and cloud-based sources alike—to create a dynamic, interconnected ecosystem (the cloth).

What is data virtualization?

Data virtualization is a method of managing data without replicating it across data pipelines. It removes the requirement of downloading, moving, or copying data by creating a virtual layer that permits users to access data as if it were in the same place. Users don’t need to know where the data is because the virtual layer enables live access directly from the data source.

If we take the metaphor to its conclusion, data virtualization can weave the threads (data) to make the finished cloth (data fabric).

Typical use cases of data fabric

Data fabric is a crucial part of modern data management, allowing seamless access, smooth integration, and timely data analysis across the organization. Here are a few well-known use cases for data fabric that address various operational needs:

  • Data discovery: A data fabric simplifies the task of locating and connecting data across various systems, whether on-site or cloud based. Data is presented in a streamlined, unified format that is easily discoverable and usable to drive timely decision-making.
  • Data integration: By enabling smooth and efficient integration with multiple data sources, a data fabric ensures that organizations have a consistent and comprehensive view of their data landscape, supporting more accurate analysis and reporting.
  • Machine learning: A data fabric provides the basis for creating the infrastructure necessary to deploy machine learning models by ensuring that these models can access and learn from a vast, integrated dataset. The consolidated data improves predictive accuracy and effectiveness.
  • Data security: With its emphasis on governance and compliance, data fabric architectures incorporate robust data security measures, safeguarding sensitive information while still making it accessible to authorized users.
  • Real-time analytics: A data fabric accelerates real-time analytics by providing immediate access to data across the organization. It enables businesses to respond swiftly to market changes and opportunities with up-to-date insights from fresh data.

Typical use cases of data virtualization

Data virtualization is gaining strength as an efficient modern data management method, recognized for its flexibility and power, offering streamlined access to live data dispersed over a variety of multiple sources. Let's go over a few common use cases:

  • Virtual data warehouses: Data virtualization helps to create virtual data warehouses, aggregating structured data from various sources without physically moving the data. This offers views into real-time analytics and reporting across diverse data systems.
  • Virtual data lakes: Organizations also use data virtualization to create virtual data lakes, which provide users with unified access to a wide range of data types stored across different environments. This setup allows for rapid intake of data, supports diverse tools and processes, and enables deeper insights.
  • Data catalogs: Data virtualization simplifies the creation of dynamic data catalogs that offer an organized view of available data across the organization. These catalogs help users discover, understand, and access data quickly, significantly improving productivity.
  • Self-service analytics: Organizations use data virtualization to provide self-service tools to employees, allowing them to perform analyses and reports without sending requests to IT teams. This helps to streamline the data management process, reducing bottlenecks and accelerating actionable insights.

What’s the difference between data fabric and data virtualization?

While both offer potent solutions to the challenges of data diversity and accessibility, understanding the key differences between data fabric and data virtualization is essential as modern organizations optimize their data strategies.

  • Architectural approach: Data fabric encompasses many data management and integration technologies, including data virtualization. It's designed to provide a comprehensive solution that facilitates data access, sharing, and analysis across an organization. Data virtualization refers specifically to the process of creating a simplified, accessible layer for querying and manipulating data from dissimilar sources without physically consolidating it.
  • Use case: While both approaches help to streamline data access and integration, data fabric's broader architectural framework is intended as an end-to-end solution that spans data governance, discovery, integration, and processing. Data virtualization is more applicable for scenarios focused primarily on overcoming the challenges of accessing and querying live data from different sources in real time.
  • Scope and complexity: Implementing a data fabric is a comprehensive effort that may require a significant overhaul of existing data management systems. It's a broad-spectrum initiative that spans various aspects of data handling, from governance to processing. Data virtualization, in contrast, is viewed as a more focused, potentially less disruptive approach, emphasizing agility and the accelerated provisioning of data access.

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