DW - Multidimensional Cube Types

As we know that, a data cube stores data in a summarised version which helps in a faster analysis of data. Based on the business requirement, you can have different types of the cubes such as given below:

Regular cubes- These are very common cubes which are based on real tables for their data source. They will have aggregations and will occupy physical storage space of some kind. If there are some changes in the contributed data source then these cubes must be reprocessed.

Virtual cubes -They are also known as logical cubes which are based on one or more regular cubes or linked cubes. Virtual cubes use the aggregations of their component regular cubes or linked cubes. This is the main reason that they not required any storage space.

Linked cubes - Linked cubes are based on regular cubes defined and stored on another Analysis Server. Linked cubes also use the aggregations and storage of the regular cube they reference. The linking in cube ensures that the data in the cubes remain consistent. Linking different data cubes reduces the possibility of sparse data. Linked cubes are the cubes that are linked in order to make the data remain constant.

Local cubes - Local cubes are entirely contained in portable files (tables) and can be browsed without a connection to an Analysis Server. They do not have aggregations. This is really like being in "disconnected" mode.

Real-time cubes - A real-time cube is a cube in which one or more Relational Online Analytical Processing Server (ROLAP) partitions or dimensions support real-time updates. Multiple dimensions or partitions can support real-time updates, and a real-time cube can have a mixture of dimensions or partitions that may or may not be enabled for real-time updates. Because of the complexity involved in managing such real-time cube data, the requirements for creating a real-time cube are more stringent than for a regular cube.

In Analysis Services, real-time Online Analytical Processing Server (OLAP) represents the capability to quickly retrieve, organise, aggregate and present multidimensional data for cubes and dimensions whenever the data changes in the underlying relational data source, without requiring the cube or dimension to be explicitly processed first.

Write-enabled cubes - The write-enabled cube is often of limited scope, receives smaller and less regular data updates, and is used by smaller numbers of more specialized (often expert) users.  These cubes are sometimes mapped to a data warehouse but more frequently are built against a specialized data mart.  On the topic of security, any end-users you wish to perform write back to the cube must be granted Read/Write permissions at the cube level.

Please visit to know more on -

1. Collaboration of OLTP and OLAP systems. 
2. Major differences between OLTP and OLAP. 
3. Data Warehouse
4. Data Warehouse - Multidimensional Cube
5. Data Warehouse - Multidimensional Cube Types
6. Data Warehouse - Architecture and Multidimensional Model. 
7. Data Warehouse - Dimension tables.
8. Data Warehouse - Fact tables.
9. Data Warehouse - Conceptual Modeling.
10. Data Warehouse - Star schema.
11. Data Warehouse - Snowflake schema.
12. Data Warehouse - Fact constellations. 
13. Data Warehouse - OLAP Servers