SSAS:概念梳理

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Dimension Objects

原文

A simple Dimension object is composed of basic information, attributes, and hierarchies.

Basic information includes the name of the dimension, the type of the dimension, the data source, the storage mode, and others.

Attributes define the actual data in the dimension.

Attributes do not necessarily belong to a hierarchy, but hierarchies are built from attributes.

A hierarchy creates ordered lists of levels, and defines the ways a user can explore the dimension.

 

原文

Dimensions are a fundamental component of cubes.

Dimensions organize data with relation to an area of interest, such as customers, stores, or employees, to users.

Dimensions in Analysis Services contain attributes that correspond to columns in dimension tables.

These attributes appear as attribute hierarchies and can be organized into user-defined hierarchies, or can be defined as parent-child hierarchies based on columns in the underlying dimension table.

Hierarchies are used to organize measures that are contained in a cube.

Cube Objects

原文

A simple Cube object is composed of: basic information, dimensions, and measure groups.

Basic information includes the name of the cube, the default measure of the cube, the data source, the storage mode, and others.

The Dimensions collection contains the actual set of dimensions used in the cube from the database dimensions colection.

All dimensions have to be defined in the dimensions collection of the database before being referenced in the cube.

Private dimensions are not available in Microsoft SQL Server Analysis Services.

Measure groups are sets of measures in the cube.

A measure group is a collection of measures that have a common data source view and a common set of dimensions.

A measure group is the unit of process for measures; measure groups can be processed individually and then browsed.

  • Actions (Analysis Services - Multidimensional Data)
  • Aggregations and Aggregation Designs
  • Calculations
  • Cube Cells (Analysis Services - Multidimensional Data)
  • Cube Properties - Multidimensional Model Programming
  • Cube Storage (Analysis Services - Multidimensional Data)
  • Cube Translations
  • Dimension Relationships
  • Key Performance Indicators (KPIs) in Multidimensional Models
  • Measures and Measure Groups

 

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