Data Virtualization enables access to physical data from various sources in a
virtual manner, so that the data can be accessed, manipulated, and analyzed from one central
location, without the need to know its physical format or location, and without having to move or
copy it.
Data Virtualization is fully integrated into Cloud Pak for Data as a Service on IBM Cloud as part of the data fabric. Data Virtualization provides the virtualization
capabilities of the data fabric architecture.
To get started, create a service instance of Data Virtualization and launch it in Cloud Pak for Data as a Service. Then, create connections to your data sources so that you can
quickly create views across all of your organization’s data.
With Data Virtualization, your company can accomplish these goals:
Simplify your analytics and make them more accurate because you’re querying the latest data at
its source.
Use real-time analytics efficiently and get current analytics for distributed data sources, with
no need to store data outside your data center.
Accelerate processing times by automatically organizing your data nodes into a collaborative
network for computational efficiency.
Take advantage of standard SQL through common interfaces such as R, Spark, Python, and Jupyter
Notebooks in a single data repository where your SQL applications can connect and run.
Centralize authentication and authorization for data sources in a trusted environment where
credentials for your private databases are stored encrypted at the local device and are private to
that device.
This service adds a workspace to Cloud Pak for Data as a Service.
Use cases
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The following table describes how Data Virtualization addresses critical needs of an organization:
Problem statement
What Data Virtualization
enables
Value
Making use of a lot of data across different locations and formats is challenging and leads
to a complex data pipeline.
A semantic layer that sits on top of the data sprawl that enables users to query across
different data sources and formats in real time.
Empower data consumers to self-service.
Storing data across different cloud and on-premises locations with software and systems that do
not work together seamlessly to create end-to-end data pipelines.
Data engineers can quickly fulfill ad hoc data integration requests to validate hypothesis or
“what-if” scenarios with security and governance.
Accelerate the data lifecycle and reduce time to value for addressing business
questions.
Inability to manage governance and enforce privacy regulations at scale.
Abstract data governance and enforce data policies across all your data sources through a
single layer.
Increase compliance with data protection regulations while reducing overhead of managing
access control at scale.
Create catalogs of curated assets with this secure enterprise catalog management platform
that is supported by a data governance framework.
Integrated services
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Table 2. Related services. The following related services are often used with this service and
provide complementary features, but they are not required.