As companies ingest more data, the challenge of integrating it becomes increasingly more complex. This is because data is now distributed across multiple applications and multiple types of data stored both on-premises, in one or more clouds, and even at the edge. Some of this data is also becoming too big to move. This makes data harder to access and integrate.

This tutorial looks to address this issue by introducing data virtualization and shows why this technology is now a key component of any information architecture. It also looks at how it enables the creation of a logical data warehouse. It discusses what data virtualization is, how it works, and the benefits it brings, as well as products and suppliers in the data virtualization marketplace. It explores best practices in setting up data virtualization, such as creating multiple layers to provide more abstraction, data independence, and agility, as well as how you can use it to simplify access to data, increase agility, and reduce time to time value.

• Introduction to data virtualization
• How does data virtualization work?
• Popular data virtualization use cases to maximise business value
• Business glossaries and data virtualization – creating a common semantic layer
• Managing performance through in-memory data caching
• Implementing layers of virtual tables to increase agility
• Enabling agile data warehouse development using data virtualization
• Modernizing your data warehouse by moving to virtual data marts instead of physical ones
• The power of virtual data sources
• Using data virtualization in a big data architecture
• Simplifying data access across data center, multiple clouds, and the edge
• Creating a logical data warehouse across multiple analytical data stores
• Serving up virtual data assets from an enterprise data marketplace
• Building an information services layer using data virtualization