Integrating Analytic Data Using Data Virtualization

More Data = Better Analysis

The analytic data domain includes all kinds of data, including:

  • traditional enterprise data from sources like the Enterprise Data Warehouse;
  • big data from sources like Hadoop;
  • cloud data from SaaS applications and public providers;
  • social media data from sites like Facebook and Twitter; and
  • personal/desktop data from spreadsheets and flat files.

Data virtualization is an optimal solution to integrate all these sources, thereby improving analysis and business insight.

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What is Your Strategy for Data Virtualization?

Five Popular Data Virtualization Usage Patterns

Data virtualization is a versatile data integration solution that can be deployed to solve a wide range of data integration challenges.  Based on nearly ten years of successful implementations, several common usage patterns have emerged to help guide your enterprise’s data virtualization adoption strategy.

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Best Practices for Integrating Cloud and On-Premise Data

Our Customers See Value in the Cloud

Our customers view Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS), and Infrastructure-as-a-Service (IaaS) as extensible functionality and computing resources at an attractive, pay-as-you-go price.   

However, cloud computing is not a “free lunch” because each new cloud source and consumer must be integrated with their existing IT environment. 

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