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.
Prepare, analyze, and model data in a collaborative environment with tools for data
scientists, developers, and domain experts.
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Cloud Pak for Data relationship map
Use this interactive map to learn about the relationships between your tasks, the tools you need, the services that provide the tools, and where you use the tools.
Select any task, tool, service, or workspace
You'll learn what you need, how to get it, and where to use it.
Some tools perform the same tasks but have different features and levels of automation.
Jupyter notebook editor
Prepare data
Visualize data
Build models
Deploy assets
Create a notebook in which you run Python, R, or Scala code to prepare, visualize, and analyze data, or build a model.
AutoAI
Build models
Automatically analyze your tabular data and generate candidate model pipelines customized for your predictive modeling problem.
SPSS Modeler
Prepare data
Visualize data
Build models
Create a visual flow that uses modeling algorithms to prepare data and build and train a model, using a guided approach to machine learning that doesn’t require coding.
Decision Optimization
Build models
Visualize data
Deploy assets
Create and manage scenarios to find the best solution to your optimization problem by comparing different combinations of your model, data, and solutions.
Data Refinery
Prepare data
Visualize data
Create a flow of ordered operations to cleanse and shape data. Visualize data to identify problems and discover insights.
Orchestration Pipelines
Prepare data
Build models
Deploy assets
Automate the model lifecycle, including preparing data, training models, and creating deployments.
RStudio
Prepare data
Build models
Deploy assets
Work with R notebooks and scripts in an integrated development environment.
Federated learning
Build models
Create a federated learning experiment to train a common model on a set of remote data sources. Share training results without sharing data.
Deployments
Deploy assets
Monitor models
Deploy and run your data science and AI solutions in a test or production environment.
Catalogs
Catalog data
Governance
Find and share your data and other assets.
Metadata import
Prepare data
Catalog data
Governance
Import asset metadata from a connection into a project or a catalog.
Metadata enrichment
Prepare data
Catalog data
Governance
Enrich imported asset metadata with business context, data profiling, and quality assessment.
Data quality rules
Prepare data
Governance
Measure and monitor the quality of your data.
Masking flow
Prepare data
Create and run masking flows to prepare copies of data assets that are masked by advanced data protection rules.
Governance
Governance
Create your business vocabulary to enrich assets and rules to protect data.
Data lineage
Governance
Track data movement and usage for transparency and determining data accuracy.
AI factsheet
Governance
Monitor models
Track AI models from request to production.
DataStage flow
Prepare data
Create a flow with a set of connectors and stages to transform and integrate data. Provide enriched and tailored information for your enterprise.
Data virtualization
Prepare data
Create a virtual table to segment or combine data from one or more tables.
OpenScale
Monitor models
Measure outcomes from your AI models and help ensure the fairness, explainability, and compliance of all your models.
Data replication
Prepare data
Replicate data to target systems with low latency, transactional integrity and optimized data capture.
Master data
Prepare data
Consolidate data from the disparate sources that fuel your business and establish a single, trusted, 360-degree view of your customers.
Services you can use
Services add features and tools to the platform.
watsonx.ai Studio
Develop powerful AI solutions with an integrated collaborative studio and industry-standard APIs and SDKs. Formerly known as Watson Studio.
watsonx.ai Runtime
Quickly build, run and manage generative AI and machine learning applications with built-in performance and scalability. Formerly known as Watson Machine Learning.
IBM Knowledge Catalog
Discover, profile, catalog, and share trusted data in your organization.
DataStage
Create ETL and data pipeline services for real-time, micro-batch, and batch data orchestration.
Data Virtualization
View, access, manipulate, and analyze your data without moving it.
Watson OpenScale
Monitor your AI models for bias, fairness, and trust with added transparency on how your AI models make decisions.
Data Replication
Provide efficient change data capture and near real-time data delivery with transactional integrity.
Match360 with Watson
Improve trust in AI pipelines by identifying duplicate records and providing reliable data about your customers, suppliers, or partners.
Manta Data Lineage
Increase data pipeline transparency so you can determine data accuracy throughout your models and systems.
Where you'll work
Collaborative workspaces contain tools for specific tasks.
Project
Where you work with data.
> Projects > View all projects
Catalog
Where you find and share assets.
> Catalogs > View all catalogs
Space
Where you deploy and run assets that are ready for testing or production.
> Deployments
Categories
Where you manage governance artifacts.
> Governance > Categories
Data virtualization
Where you virtualize data.
> Data > Data virtualization
Master data
Where you consolidate data into a 360 degree view.