[3] To resolve differences in source and consumer formats and semantics, various abstraction and transformation techniques are used.
Furthermore, avoiding the creation of a new database containing personal information can make it easier to comply with privacy regulations.
If the desired outcome changes, updating the description suffices, and the software adjusts the intermediate steps accordingly.
This flexibility can accelerate processes by up to five times, underscoring the primary advantage of data virtualization.
Connection problems occur more often in complex systems where one or more crucial sources will occasionally be unavailable.
Virtualization makes it possible to combine personal data from different sources without physically copying them to another location while also limiting the view to all other collected variables.
However, virtualization does not eliminate the requirement to confirm the security and privacy of the analysis results before making them more widely available.
[8][clarification needed] Benefits include: Drawbacks include: Avoid usage: Enterprise information integration (EII) (first coined by Metamatrix), now known as Red Hat JBoss Data Virtualization, and federated database systems are terms used by some vendors to describe a core element of data virtualization: the capability to create relational JOINs in a federated VIEW.