Data Virtualization: Solving Complex Data Integration Challenges
The data integration landscape has changed radically the past few years. What was once a relatively manageable problem of blending and unifying data from enterprise transaction systems has grown to encompass external data, Web data, clickstream data, end-user data, big data, cloud data, and more. New expectations for information-driven business agility further compound the complexities of modern data integration. The ETL-based data warehouse is no longer enough. Data virtualization is a core component of next-generation data integration architectures, techniques, and technology.
Get ready to expand your data integration capabilities, deliver business-speed information, and make the most of recent advances in data integration technology. Through a combination of lecture, exercises, and case study review you will learn how data virtualization works and how to position it in your data integration architecture and processes.
You Will Learn
- Data virtualization definitions and terminology
- Business case and technical rationale for data virtualization
- Key concepts and foundational principles of virtualization–views, services, etc.
- Data virtualization lifecycle, capabilities, and processes
- How to extend the data warehouse with virtualization
- How virtualization enables federation and enterprise data integration
- How virtualization is applied for big data and cloud data challenges
- How companies use virtualization to solve business problems and drive business agility
- BI, MDM, and data warehousing program and project managers
- Data integration architects, designers, and developers
- Data and technology architects
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