However, data virtualization may be extended and adapted to serve data warehousing requirements also. Data virtualization is inherently aimed at producing quick and timely insights from multiple sources without having to embark on a major data project with extensive ETL and data storage. ĭata virtualization may also be considered as an alternative to ETL and data warehousing but for performance considerations it's not really recommended for a very large data warehouse. This aspect of data virtualization makes it complementary to all existing data sources and increases the availability and usage of enterprise data.
Existing data infrastructure can continue performing their core functions while the data virtualization layer just leverages the data from those sources. Data virtualization can efficiently bridge data across data warehouses, data marts, and data lakes without having to create a whole new integrated physical data platform. Some enterprise landscapes are filled with disparate data sources including multiple data warehouses, data marts, and/or data lakes, even though a Data Warehouse, if implemented correctly, should be unique and a single source of truth. 1 Data virtualization and data warehousingĭata virtualization and data warehousing.This concept and software is a subset of data integration and is commonly used within business intelligence, service-oriented architecture data services, cloud computing, enterprise search, and master data management.
To resolve differences in source and consumer formats and semantics, various abstraction and transformation techniques are used. The technology also supports the writing of transaction data updates back to the source systems. This reduces the risk of data errors, of the workload moving data around that may never be used, and it does not attempt to impose a single data model on the data (an example of heterogeneous data is a federated database system). Unlike the traditional extract, transform, load ("ETL") process, the data remains in place, and real-time access is given to the source system for the data. Data virtualization is an approach to data management that allows an application to retrieve and manipulate data without requiring technical details about the data, such as how it is formatted at source, or where it is physically located, and can provide a single customer view (or single view of any other entity) of the overall data.
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