Dynamic Data Virtualization.

Go beyond traditional data virtualization by selecting what, where, when, and how  to virtualize.


Dynamic Data Virtualization
for agile data integration

icons_Data Virtualization-Perform Live

Perform live

Connect to data, and deliver it 
in milliseconds 

icons_Data Virtualization-Have it your way

Have it your way

Choose which data to virtualize, 
and which to store

Data Virtualization Enrichment

Inject insights

Enrich data in real-time, 
for operational intelligence

K2View Dynamic Data Virtualization

A unique virtual view

Dynamic Data  Virtualization

K2View Dynamic Data Virtualization ingests data from any source, unifies it according to a semantic layer, optionally stores it (physically or in memory), transforms and enriches it, and finally makes it available to consuming applications and data analysts.

Get more out of
data virtualization tools


Turn data into business
with data abstraction

Dynamic Data Virtualization takes the complexity out of accessing data from a wide range of underlying data sources, formats and structures.

It provides access to live data through a logical abstraction layer whose schema is the collection of all tables and fields for a particular business entity. We call this abstraction layer a data product schema, and our platform can manage multiple schemas (for multiple business entities), including the relationships between them.

In the case of a customer business entity, for example, the customer data product includes a schema that unifies all the information about a customer from all underlying systems.

Virtualize or store –
it’s your call


Dynamic Data Virtualization gives you complete flexibility to decide which of the data product’s data will be virtualized – unified, transformed, orchestrated, and delivered from source to target – and which data will be stored physically in a Micro-Database™ along the way.

Storing some of the data, rather than completely virtualizing it, is an advantage when you’d like to:

  • Minimize the burden on your source systems, by storing data that doesn’t change on an ongoing basis, instead of accessing it from the source every time.
  • Enrich the data with new fields that do not exist in your underlying source applications.
  • Implement Reverse ETL.
  • Maintain a log for the changes made to the data product instance, to compare its content over time (e.g., in tracking configuration changes made to a specific device at a customer's site).

Top-Rated on Gartner

See how Vodafone implemented K2View Data Fabric for multiple use cases, including Customer Data Hub and Test Data Management, to deliver strategic business
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Access the data in any method

K2View Data Product Platform enables authorized data consumers with seamless access to the data they need via SQL, or web service APIs. Alternatively, the data can be delivered ("pushed") to the data consumers via data streaming or messaging methods.

Dynamic Data Virtualization assures unmatched high-speed, high-scale data delivery, from source systems to data consumers.

Get more out of
data virtualization tools

K2View Dynamic Data Virtualization tools hand the controls over to you.