Data Virtualization software accesses and extracts information from target sources at runtime and combines them in real time to get the results. As you know, no local copy of the data will be available within Denodo.
With this in mind, it is clear that some of the more traditional performance optimization practices used in database and data warehouse implementations, such as index construction, will fall outside the scope of a real-time Data Virtualization framework but strategies such as caching can help to improve the performance of real time source access and combination goals.
The Denodo advanced cache system is based on a relational database (traditional or in-memory database).
Denodo is an important component of any data management infrastructure, but not the only one. When measuring performance, it is important to make sure which of the elements are bottlenecks. For example, a data source might be returning data in a slow fashion; in some cases you will be able to increase the performance by adding a new index to that source. If these actions cannot be perfomed, you can configure an intelligent caching system in Denodo to speed up your queries.
So, summarizing, what are the motivations for using cache?
In this section you will see how to: