Data Virtualization
Data virtualization is a data integration approach that provides a unified, abstracted view of data from multiple disparate sources (such as databases, cloud storage, APIs, or files) without physically moving or replicating the data. It creates a virtual layer that allows applications to query and access data in real-time as if it were stored in a single location, while the data remains in its original sources. This enables faster access to distributed data, reduces data redundancy, and simplifies data management across complex environments.
Developers should learn and use data virtualization when building applications that need to integrate data from multiple heterogeneous sources (e.g., combining on-premises databases with cloud services) without the overhead of ETL processes or data warehousing. It is particularly useful in scenarios requiring real-time data access, such as business intelligence dashboards, microservices architectures, or legacy system modernization, as it improves agility and reduces storage costs. For example, in a microservices setup, data virtualization can provide a unified API for services to query data from various backends without coupling them to specific data stores.