Data Virtualization
Description
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
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. |
Quick links
- Set up: Set up the service
- Administer: Manage and maintain the service
- Use: Work with the service
- Develop: Write code and build applications
- What's new: See what's new each week
- Create: Create the service instance
Required services
| Service | Capability |
|---|---|
| IBM Knowledge Catalog | Create catalogs of curated assets with this secure enterprise catalog management platform that is supported by a data governance framework. |
Integrated services
| Service | Capability |
|---|---|
| watsonx.ai™ Studio | Prepare, analyze, and model data in a collaborative environment with tools for data scientists, developers, and domain experts. |
Compatible data sources
See Supported data sources in Data Virtualization for a list of data sources that are compatible.