A Logical Data Layer For Any Data, Anywhere

Remove data bottlenecks with a consolidated connectivity layer that unifies data access across data sources.

Data Virtualization Diagram

The Growing Problem Of Data Fragmentation

Data Fragmentation Diagram

Historically, BI and data integration tools only had a handful of data sources to integrate and connect with. SaaS and cloud technologies have created a very different looking market landscape. Data is locked in multiple clouds, apps, databases and legacy platforms, creating data silos.

Moving data around in these environments is a slow process, posing a challenge for business analytics. Data can sometimes change by the time it is replicated and integrated which leads to inaccurate analysis.

Data Virtualization

Drawing value from massive amounts of siloed data is an arduous task, but data virtualization solves that problem. Data virtualization is the ability to orchestrate data in real-time or near real-time from disparate data sources, whether on-premises or cloud, into coherent self-service data services to support various business use cases and workloads.

The most common approach to data virtualization is to deploy a stand-alone server that acts like a logical data warehouse layer for data connectivity. This layer becomes the interface that all data sources connect through.

Embedded Data Virtualization shares many of the same characteristics of stand-alone DV. However, instead of one external platform, systems are augmented with a common unified data access layer.

Data Virtualization Diagram

Stand Alone Data Virtualization

Stand alone data virtualization solutions are a common approach to logical data warehousing. However, many of these systems are large, costly and difficult to maintain. Furthermore, a project of this scale typically needs to be endorsed by the entire IT organization.

Stand Alone Data Virtualization Diagram

Embedded Data Virtualization

Unlike stand-alone Data Virtualization solutions, Embedded DV can be deployed as a tactical component of other applications. Analytics or integration solutions can leverage Embedded DV as an interface for extensibility and can pick and choose the features that matter most for their applications.

The CData Drivers provide some of the components of Embedded DV, offering a common SQL interface on top of disparate data. However, a common requirement for working with data is the ability to join data across sources. For that scenario, users can leverage CData Connect Cloud.

Benefits of Embedded Data Virtualization

  • Simplified Application Development - Developers can pick multiple data processing systems and access all of them with a single SQL-based interface.
  • Query Across Multiple Systems - In many ad-hoc scenarios, it is impractical to build pipelines to consolidate data. With query federation, you can write queries that combine data from different sources directly, on-demand.
  • Up to 85% Faster - Faster and easier data connectivity vs. traditional data warehousing and ETL.

Embeded Data Virtualization Diagram

Embedded DV in Action

Watch the Query Federation Driver overview video for a first-hand look at how applications can simplify and consolidate data connectivity through a common interface:


Learn more about CData Connect Cloud.

Drivers & Data Virtualization

Want to learn more about how CData Drivers and Adapters enhance data connectivity through data virtualization technologies? Contact us below, and let's talk.

Contact Us