Data Virtualization and the Data Fabric: Integrating Disparate Data Sources

Introduction

In today's data-driven world, organizations are faced with the challenge of managing and integrating data from a growing number of heterogeneous sources, including on-premises databases, cloud-based applications, data lakes, and external data providers. This proliferation of data sources can make it difficult for data consumers to find, access, and understand the data they need to make informed decisions.

Data virtualization is a design pattern that addresses this challenge by providing a unified, abstracted view of data from multiple, disparate sources. By creating a virtual data layer that sits between the data sources and the data consumers, data virtualization enables seamless, self-service access to data without the need for complex data integration or data movement processes.

Data Virtualization: The Unified Data Access Layer

The core idea behind data virtualization is to create a logical, virtual data layer that presents a consolidated, consistent view of data to data consumers, regardless of the underlying data sources. This virtual data layer acts as an abstraction, hiding the complexity of the underlying data sources and providing a standardized interface for querying and accessing data.