Data Virtualization

Access, combine, and provision all your enterprise data

Organizations have data in silos spread across traditional data warehouses, enterprise applications, big data lakes, operational data stores, the cloud, and more, creating challenges for business teams.

Data virtualization breaks down data silos delivering one place to access, combine, and provision all your data. Business-friendly data views simplify access and hide IT complexity. Agile data engineering speeds time-to-solution, reduces costs, and delivers the most up-to-date and complete information to your business.

Use Cases
Data Integration

Data Integration

Connect disjointed and diverse data across hybrid environments to achieve organizational goals. Solve business problems and reduce costs while boosting efficiency, productivity, innovation, and growth.

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Cloud Data Migration

Cloud Data Migration

Reduce the time, cost, and risk of your enterprise data migration projects. Move any amount of data between any two supported systems (on-premises or the cloud) with no limits on usage while easily collaborating with consultants and others who may be involved in the project.

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Data Fabric

Data Fabric

Optimize data management and integration capabilities so you can intelligently simplify, automate, and accelerate your data pipelines. Share data assets that support all your diverse users and use cases. Easily deploy and adapt a distributed data architecture that fits your complex, ever-changing technology landscape.

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Create an enterprise-grade data foundation that facilitates cross-silo data integration, allows ingestion from external data providers, hides complexity, and supports API security, manageability, and governance.

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Empower everyone

Virtual Data Layer

Create a virtual data layer with semantic consistency by combining your diverse, distributed data sources, on-premises or in the cloud.

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Simplify the complex

Logical Data Warehouse

Turn diversity into opportunity by deploying a common analytic data management architecture across diverse data types, technologies, users, and use cases.

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