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

Data fabric and data virtualization are two innovative methodologies that offer flexible solutions to modern organizations' challenges of managing diverse and accessible data. Data fabric is a unified architectural approach that provides seamless access to data from multiple sources, while data virtualization creates a simplified layer for querying and manipulating data without physically consolidating it. Data fabric encompasses various technologies, including data virtualization, and offers an end-to-end solution for data governance, discovery, integration, and processing. In contrast, data virtualization is a more focused approach that emphasizes agility and real-time access to live data from different sources. While both approaches help streamline data access and integration, understanding their differences is crucial for modern organizations to optimize their data strategies.