Six Strategies for Simplifying Hybrid, Multicloud Data Integration
How to exploit data virtualization to provide true data transparency across multiple cloud and on-premises environments
This TDWI Checklist discusses why data virtualization is relevant to the challenges of hybrid, multicloud environments. Rather than depend on moving data to a central location, a data virtualization layer connects to the sources to access metadata, which is then used to enable data transformations and joins that result in a new, logical data source. Data virtualization solutions can also utilize data catalogs and metadata repositories that are either embedded in the data virtualization solution or come from an independent software vendor. Among other things, data virtualization solutions can apply the information about the data found in catalogs and metadata repositories to improve the breadth and speed of queries across multiple systems. Modern data virtualization solutions have invested in query optimization to ensure that the shortest path Driven is taken to and from the data, which minimizes network traffic—a critical matter in making cloud computing fast and cost-effective.
For users, data virtualization presents an abstraction layer, shielding them from the complexities of the various source data formats and implementations. A data virtualization layer can enable BI and analytics users, as well as applications and artificial intelligence (AI) programs, to transparently access the numerous data silos present in hybrid, multicloud environments without having to wait for the data to be moved into a data warehouse or data lake. A data virtualization layer situated in the cloud, on premises, or both can improve governance and security, as this Checklist will discuss.
This TDWI Checklist presents six strategies for using data virtualization to simplify hybrid, multicloud data integration and governance.