Best Data Virtualization Software

Compare the Top Data Virtualization Software as of June 2025

What is Data Virtualization Software?

Data virtualization tools allow IT teams to enable applications to view and access data while obscuring the location of the data, and other identifying aspects of the data. Data virtualization software enables the use of virtual data layers. Compare and read user reviews of the best Data Virtualization software currently available using the table below. This list is updated regularly.

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    K2View

    K2View

    K2View

    At K2View, we believe that every enterprise should be able to leverage its data to become as disruptive and agile as the best companies in its industry. We make this possible through our patented Data Product Platform, which creates and manages a complete and compliant dataset for every business entity – on demand, and in real time. The dataset is always in sync with its underlying sources, adapts to changes in the source structures, and is instantly accessible to any authorized data consumer. Data Product Platform fuels many operational use cases, including customer 360, data masking and tokenization, test data management, data migration, legacy application modernization, data pipelining and more – to deliver business outcomes in less than half the time, and at half the cost, of any other alternative. The platform inherently supports modern data architectures – data mesh, data fabric, and data hub – and deploys in cloud, on-premise, or hybrid environments.
  • 2
    VeloX Software Suite

    VeloX Software Suite

    Bureau Of Innovative Projects

    VeloX Software Suite enables Data Migration and System Integration throughout the entire organization. The suite consists of two applications, Migration Studio (VXm) for user-controlled data migrations; Integration Server (VXi), for automated data processing and integration. Extract from multiple sources and propagate to multiple destinations. Near real-time unified view of data without moving between sources. Physically bring data together from a multitude of sources, reduce the number of data storage locations, and transform based on business rules. Extract from multiple sources and propagate to multiple destinations. Event- and rules-driven. Synchronous and asynchronous exchange. EAI and EDR technologies. Near real-time unified view of data without moving between sources. Service-oriented architecture. Various abstraction and transformation techniques. EII technologies.
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