Data virtualization uses a simple three-step process—connect, combine, consume—to deliver a holistic view of enterprise information to business users across all of the underlying source systems.
Connect to Any Data Source
Data virtualization connects to all types of data sources—databases, data warehouses, cloud applications, big data repositories, and even Excel files.
The Connect layer accesses information from the various repositories and masks the complexities of the underlying communication protocols and formats from the upper layers. Data virtualization connects to the widest range of data sources, from structured to unstructured, including databases, big data systems, streaming sources, cloud repositories, the Web, NoSQL sources, and flat files. It uses specialized connectors to access specific data repositories or applications and carries out data source type conversions and normalization so that all base views appear as relational views to the upper layers.
Combine Any Type of Data
Data virtualization combines the related information into business views irrespective of their data format—relational databases, noSQL, Hadoop, web services and Cloud APIs, files, etc.
The Combine layer flexibly automates Web integration processes that are modeled using a library of pre-built templates and components for workflow, navigation, and extraction as well as the structuring of Web, semi-structured, and unstructured data. It supports data transformations with logical operators for the seamless creation of composite data views on top of the base views delivered by the connect layer. In this layer, users can perform complex data transformations, metadata modeling, and data quality and semantic matching operations using SQL and relational tools that they are already familiar with.
Consume the Data in Any Mode
Data virtualization enables business users to consume data through reports, dashboards, portals, mobile apps, and Web applications.
The Consume layer enables a single point of access and interaction with the underlying data sources, as well as abstracted data views in a standard delivery format. The Consume layer offers the broadest data delivery options to suit business user needs via JDBC, ODBC, ADO.NET, SOAP web services, RESTful web services (output as XML, JSON, HTML, or RSS), OData, portlets and data widgets (JSR-168, JSR-286, or Microsoft Web Parts to be deployed in SharePoint), exports to Microsoft Excel/SQL, and JMS message queues.
Data virtualization is a unified, virtual data layer with which enterprise applications and users can access any enterprise information regardless of its location, format, or protocol, using the methods that best suit their work needs such as data discovery and search.
See It in Action
Data Virtualization Use CasesData virtualization supports many critical use cases within an organization. Here are the key solutions enabled by data virtualization:
Customer Centricity / MDM
- A complete view of the customer
- Data Privacy / Masking
- Data as a Service
- Data Marketplace
- Data Services
- Application and Data Migration
BI and Analytics
- Self-Service Analytics
- Logical Data Warehouse
- Enterprise Data Fabric
- Logical Data Lake
- Data Warehouse offloading
- IoT Analytics
- Cloud Modernization
- Cloud Analytics
- Hybrid Data Fabric
Learn details about how data virtualization enables these solutions.
Also, data virtualization supports solutions that are specific to many industries such as banking, insurance, and oil and gas. Learn how data virtualization can help you in your industry.