Partitions in Multidimensional Models (SSAS, SQL Server 2014)

In Analysis Services, a partition provides the physical storage of fact data loaded into a measure group. A single partition is created for each measure group automatically, but it is common to create additional partitions that further segment the data, resulting in more efficient processing and faster query performance.

Processing is more efficient because partitions can be processed independently and in parallel, on one or more servers. Queries run faster because each partition can be configured to have storage modes and aggregation optimizations that result in shorter response times. For example, choosing MOLAP storage for partitions containing newer data is typically faster than ROLAP. Likewise, if you partition by date, partitions containing newer data can have more optimizations than partitions containing older data that is accessed less frequently. Note that varying storage and aggregation design by partition will have a negative impact on future merge operations. Be sure to consider whether merging is an essential component of your partition management strategy before optimizing individual partitions.

Dimensions in Multidimensional Models (SSAS, SQL Server 2014)

A database dimension is a collection of related objects, called attributes, which can be used to provide information about fact data in one or more cubes. For example, typical attributes in a product dimension might be product name, product category, product line, product size, and product price. These objects are bound to one or more columns in one or more tables in a data source view. By default, these attributes are visible as attribute hierarchies and can be used to understand the fact data in a cube. Attributes can be organized into user-defined hierarchies that provide navigational paths to assist users when browsing the data in a cube.

Cubes contain all the dimensions on which users base their analyses of fact data. An instance of a database dimension in a cube is called a cube dimension and relates to one or more measure groups in the cube. A database dimension can be used multiple times in a cube. For example, a fact table can have multiple time-related facts, and a separate cube dimension can be defined to assist in analyzing each time-related fact. However, only one time-related database dimension needs to exist, which also means that only one time-related relational database table needs to exist to support multiple cube dimensions based on time.

Cubes in Multidimensional Models (SSAS, Sql Server 2014)

A cube is a multidimensional structure that contains information for analytical purposes; the main constituents of a cube are dimensions and measures. Dimensions define the structure of the cube that you use to slice and dice over, and measures provide aggregated numerical values of interest to the end user. As a logical structure, a cube allows a client application to retrieve values, of measures, as if they were contained in cells in the cube; cells are defined for every possible summarized value. A cell, in the cube, is defined by the intersection of dimension members and contains the aggregated values of the measures at that specific intersection.

Data Source Views in Multidimensional Models (DSV in SSAS) Sql Server 2014

A data source view (DSV) is an abstraction of a relational data source that becomes the basis of the cubes and dimensions you create in a multidimensional project. The purpose of a DSV is to give you control over the data structures used in your project, and to work independently of the underlying data sources (for example, the ability to rename or concatenate columns without directly modifying the original data source).

You can build multiple data source views in an Analysis Services project or database on one or more data sources, and construct each one to satisfy the requirements for a different solution.

What is SSAS? (SQL Server Analysis Services)

SQL Server Analysis Services (SSAS) delivers online analytical processing (OLAP) and data mining functionality for business intelligence applications. Analysis Services supports OLAP by letting you design, create, and manage multidimensional structures that contain data aggregated from other data sources, such as relational databases. For data mining applications, Analysis Services lets you design, create, and visualize data mining models that are constructed from other data sources by using a wide variety of industry-standard data mining algorithms.