Virtual Cubes and their Significance in Mondrian

Posted on by By Nikhilesh, in Business Intelligence, Helical Insight, Jaspersoft, Open Source Business Intelligence, Pentaho | 0

What is a Virtual Cube and What is its Significance in Mondrian

Before going into the Virtual cubes, first lets just refresh our memory about what is a cube. A cube is basically a multi dimensional views of data,querying and analytical compoenets to clients. Its is a data processing unit composed of dimensions and fact tables. It acts as a business layer between an end user and the DW. A cube works on the mechanism of Star schema where there can be a fact table and multiple dimension tables and all the dimensions are related to the fact.

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Virtual Cubes:

We all now know that each cube can have one fact table and all the dimensions present in the cube are having a relationship with that fact table. Now there are some scenarios in which we have two different cubes and we need to establish a relationship between them. In those kind of scenarios, we have the virtual cubes concept. A virtual cube is a cube in which we can use the dimensions and measures from multiple cubes and generate a analysis report using that cube. if we need to analyze data across multiple cubes, or need to combine information from two fact tables on the same subject but with different granularity — then you must create a virtual cube.

Significance of Virtual cubes:

1. A virtual cube can also be based on a single cube to expose only selected subsets of its measures and dimensions.

2. A virtual cube can include normal or linked cubes as component cubes.

3. Since virtual cubes store only their definitions and not the data of their component cubes, they require virtually no physical storage space. You can use virtual cubes to create combinations and variants of existing cubes without using significant additional storage.

4. A virtual cube can provide a valuable security function by limiting the access of some users when viewing the underlying cubes. If some of a cube’s information is sensitive and not suitable for all users, you can create a virtual cube from the existing cube and omit the sensitive information.

5. Virtual cubes can also be used to implement the concept of Snowflake schema. In virtual cubes, we can combine the information from the fact table on the same subject and also information between two dimension from different cubes. it improves the performance and consumes lesser memory.

implementation of Virtual cube:

1. Dimensions which are defined outside the cube (non-confirmed dimensions) can be used directly inside the virtual cube. However, the dimensions which are defined inside the cube can be accessed by adding the cube name inside the VirtualCubeDimension component.

2. The VirtualCubeMeasure component imports a measure from one of the constituent cubes. It is imported with the same name. If you want to create a formula or rename a measure as you import it, use the CalculatedMember component instead.

However, virtual cubes has a disadvantage if the elements present inside the cube is large in number. Processing a virtual cube automatically triggers processing of all underlying cubes that need to be processed, which can add significant time. So it is advisable to use lesser amount of components and only those components that are actually required in the virtual cube.


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