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Transparent Data Virtualization - FAQ's
Frequently Asked Questions About Transparent Data Virtualization
Transparent data virtualization enables enterprise data sharing by providing simple, virtual access to data throughout the organization. Rather than developing and maintaining data pipelines or replication jobs to copy data from silo to silo, data can be queried directly from its source.
Unlike many data virtualization products that require a federation engine to translate between variations of datastores and force code changes to existing applications, transparent data virtualization makes data access transparent to applications and end users without code changes, regardless of where the underlying data lives.
The main difference between standard data virtualization and transparent data virtualization is that the latter is completely transparent to the end users and requires zero code changes. Transparent data virtualization is the opposite of data federation as it uses a centralized data store to allow data access to remain transparent while the location of the data storage and query processing engine changes automatically.
Any company that wants to share data across disparate systems or is planning a data modernization effort will benefit from transparent data virtualization. However, large enterprises often benefit the most from transparent data virtualization due to the increased number of data silos and difficulty in sharing data throughout the organization.
Common use cases for Gluent Data Platform’s transparent data virtualization are:
- Accelerating cloud migrations
- Enterprise data sharing
- Eliminating enterprise data sprawl
- Active archiving of historical data
- Integrating IoT data, machine learning results and other big data sources back to legacy RDBMS applications
Benefits of using Gluent Data Platform for transparent data virtualization include:
- Faster adoption of cloud platforms and modern data architectures
- Cost reduction in storage, compute and data movement processes
- Decreased RDBMS license cost due to having a smaller footprint
- Enhanced capabilities, such as machine learning across previously siloed datasets
- Transparent access to virtualized data allows applications to continue operating with zero code changes
- Improved application performance by leveraging modern, scalable infrastructure without refactoring existing applications