DATA VIRTUALIZATION

DATA VIRTUALIZATION

Data virtualization software acts as a bridge across multiple, diverse data sources, bringing critical decision-making data together in one virtual place to fuel analytics.

Data virtualization provides a modern data layer that enables users to access, combine, transform, and deliver datasets with breakthrough speed and cost-effectiveness. Data virtualization technology gives users fast access to data housed throughout the enterprise—including in traditional databases, big data sources, and cloud and IoT systems—at a fraction of physical warehousing and extract/transform/load (ETL) time and cost.

With data virtualization, users can apply a range of analytics—including visualized, predictive, and streaming analytics—on fresh, up-to-the-minute data updates. Through integrated governance and security, data virtualization users are assured their data is consistent, high quality, and protected. Additionally, data virtualization allows for more business-friendly data, transforming native IT structures and syntax into easy-to-understand, IT-curated data services that are easy to find and use via a self-service business directory.

Data virtualization supports multiple lines of business, hundreds of projects, and thousands of users that can increase from project to enterprise scale.

Common data sources virtualized through data virtualization software

  • Packaged apps
  • RDBMS
  • Excel & flat files
  • Data warehouses
  • Data lakes
  • Big data
  • XML docs
  • Cloud data
  • Web services
  • IoT data

Article content

Common systems used with data virtualization

  • Oracle
  • SQL Server
  • Teradata
  • Netezza
  • DB2
  • Hive
  • Impala
  • Sharepoint
  • Excel
  • Flat files
  • Amazon Redshift
  • Google Big Query
  • Spark
  • Drill
  • REST
  • OData


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