The Target Designer provides an interface to enable you to create and edit cubes and dimensions. Multidimensional metadata refers to the logical organization of data used for analysis in online analytical processing (OLAP) applications. This logical organization is generally specialized for the most efficient data representation and access by end users of the OLAP application. The following sections provide an overview of the concepts relevant to the multi-dimensional features of PowerCenter.
Understanding Multi-Dimensional Metadata
The multi-dimensional model is a key aspect of data warehouse design. A well-designed dimensional model can help you organize large amounts of data. The dimensional model was originally created for the retail industry, where analysts view business data by simple dimensions, such as products and geographies. This dimensional model consists of a large central fact table and smaller dimension tables. The fact table contains the measurable facts, such as total sales and units sold, and disjoint dimensions represent the attributes pertaining to various business segments of the industry. The central fact table is the only table in the schema with multiple joins connecting it to the dimension tables. The dimension tables in turn each have a single join connecting them to the central fact table.
There are different types of multi-dimensional models depending on the degree of redundancy in the logical schema. More redundancy can improve the efficiency of data access but represents a less normalized view of the logical schema. The most common type of a multi-dimensional schema is called a star schema. A star schema is a normalized multi-dimensional model where each of its disjoint dimensions is represented in a single table.
Another type of a normalized multi-dimensional model is a snowflake schema. A snowflake schema is logically similar to a star-schema except that at least one dimension is represented in two or more hierarchically-related tables. The star schema can become a snowflake schema if the product dimension is represented by means of multiple tables. For example, you could add one dimension table for the main product attributes, one for the brand attributes, and one for a specific brand attributes.
Non-normalized multi-dimensional models have duplicate attributes in tables that are associated with a dimension. You can quickly retrieve various attributes of a dimension without having to perform multiple joins between tables in the dimension.
Key Elements of Multi-Dimensional Metadata
The following table describes key elements of multi-dimensional metadata:
Using The Designer
Working With Sources
Working With Flat Files
Working With Targets
Mapping Parameters And Variables
Working With User-defined Functions
Using The Debugger
Viewing Data Lineage
Managing Business Components
Creating Cubes And Dimensions
Using The Mapping Wizards
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