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VDI (Virtual Data Integration)
What is virtual integration and when is it appropriate to use? Virtual data integration (VDI) is the use of a software layer that interfaces with the reporting or end-user delivery application, in place of direct access of a data repository. The software layer provides a mapping between the conceptual data model that the user interfaces with, and the physical model(s) and underlying data stores. The reason I say “model(s)” is that one of the most powerful aspects of VDI is the ability to make multiple data stores (whether they be transactional, ODS, data warehouse/marts, or others) look like one integrated repository. Below are the pros and cons of using VDI verses physical data integration. Pros: • Shortens implementation time since data does not have to be physically integrated • Provides short term benefit to the business while buying time for technical team to integrate/retire existing redundant and fragmented data architecture • Can reduce data storage requirements • Source data updates are available immediately to end-users • Eliminates cost and time associated with extraction and movement of data (e.g., no batch window) • Business rules can be modified “on the fly”
Cons: • Data is not accessible if source system is unavailable • Complex transformations and aggregations can impact report response time • Updates to source systems can result in out of sync data on reports • Complex match-merge routines may not be possible outside of batch processing • Accessing large data sets can impact report response time • Query processing can impact source system performance
In my mind, the key differentiator is the volume of data being analyzed. If end users are accessing relatively few numbers of records, a VDI solution is viable. But when large data volumes need to be accessed, such as in a data mining or long term trending exercise, performance may become an insurmountable issue, both from the end user as well as the source system owner perspective.
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