What is OLAP cube:-
An OLAP cube is a collection of measures (facts) and dimensions from the data warehouse.
An OLAP cube is a multidimensional database that is optimized for data warehouse and online analytical processing (OLAP) applications.
An OLAP cube is a method of storing data in a multidimensional form, generally for reporting purposes.
In OLAP cubes, data (measures) are categorized by dimensions.
Data and aggregations are stored in a (pre-summarized across dimensions) optimized format to offer very fast query performance.
MDX (multidimensional expressions) query language is used to interact and perform tasks with OLAP cubes.
The MDX language was originally developed by Microsoft in the late 1990s, and has been adopted by many other vendors of multidimensional databases.
Although it stores data like a traditional database does, an OLAP cube is structured very differently.
OLAP cubes, however, are used by business users for advanced analytics.
Thus, OLAP cubes are designed using business logic and understanding.
They are optimized for analytical purposes, so that they can report on millions of records at a time.
When to use CUBE-
There are three reasons for adding a cube to your solution:
1. Performance- A cube’s structure and pre-aggregation allows it to provide very fast responses to queries that would have required reading, grouping and summarizing millions of rows of relational star-schema data.
The drilling and slicing and dicing that an analyst would want to perform to explore the data would be immediate using a cube but it could take longer when using a relational data source.
2. Drill down functionality- Many reporting software tools will automatically allow drilling up and down on dimensions with the data source is an OLAP cube.
Some tools, like IBM Cognos’ Dimensionally Modeled Relational model will allow you to use their product on a relational source and drill down as if it were OLAP but you would not have the performance gains you would enjoy from a cube.
3. Availability of software tools- Some client software reporting tools will only use an OLAP data source for reporting. These tools are designed for multi-dimensional analysis and use MDX behind the scenes to query the data.
Disadvantage of using SSAS cube-
SSAS processes data from the underlying relational database into the cube.
After this is done the cube is no longer connected to the relational database so changes to this database will not be reflected in the cube.
Only when the cube is processed again, the data in the cube will be refreshed.
So Basically what the cube is:-
A cube is a structure made of number of dimensions, measures, etc.
Cubes usually rely on two kind of tables like 'fact-table' (for cubes) & 'dimension-table' (for cube’s different dimensions).
A cube can have only one fact-table and ‘n’ number of dimension tables (based on no: of dimensions in the cube).
It store the data in pre-aggreagted form.
see also storage type`s of cube MOLAP, ROLAP, HOLAP
An OLAP cube is a collection of measures (facts) and dimensions from the data warehouse.
An OLAP cube is a multidimensional database that is optimized for data warehouse and online analytical processing (OLAP) applications.
An OLAP cube is a method of storing data in a multidimensional form, generally for reporting purposes.
In OLAP cubes, data (measures) are categorized by dimensions.
Data and aggregations are stored in a (pre-summarized across dimensions) optimized format to offer very fast query performance.
MDX (multidimensional expressions) query language is used to interact and perform tasks with OLAP cubes.
The MDX language was originally developed by Microsoft in the late 1990s, and has been adopted by many other vendors of multidimensional databases.
Although it stores data like a traditional database does, an OLAP cube is structured very differently.
OLAP cubes, however, are used by business users for advanced analytics.
Thus, OLAP cubes are designed using business logic and understanding.
They are optimized for analytical purposes, so that they can report on millions of records at a time.
When to use CUBE-
There are three reasons for adding a cube to your solution:
1. Performance- A cube’s structure and pre-aggregation allows it to provide very fast responses to queries that would have required reading, grouping and summarizing millions of rows of relational star-schema data.
The drilling and slicing and dicing that an analyst would want to perform to explore the data would be immediate using a cube but it could take longer when using a relational data source.
2. Drill down functionality- Many reporting software tools will automatically allow drilling up and down on dimensions with the data source is an OLAP cube.
Some tools, like IBM Cognos’ Dimensionally Modeled Relational model will allow you to use their product on a relational source and drill down as if it were OLAP but you would not have the performance gains you would enjoy from a cube.
3. Availability of software tools- Some client software reporting tools will only use an OLAP data source for reporting. These tools are designed for multi-dimensional analysis and use MDX behind the scenes to query the data.
Disadvantage of using SSAS cube-
SSAS processes data from the underlying relational database into the cube.
After this is done the cube is no longer connected to the relational database so changes to this database will not be reflected in the cube.
Only when the cube is processed again, the data in the cube will be refreshed.
So Basically what the cube is:-
A cube is a structure made of number of dimensions, measures, etc.
Cubes usually rely on two kind of tables like 'fact-table' (for cubes) & 'dimension-table' (for cube’s different dimensions).
A cube can have only one fact-table and ‘n’ number of dimension tables (based on no: of dimensions in the cube).
It store the data in pre-aggreagted form.
see also storage type`s of cube MOLAP, ROLAP, HOLAP
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