Table of Contents


  1. Preface
  2. Using the Designer
  3. Working with Sources
  4. Working with Flat Files
  5. Working with Targets
  6. Mappings
  7. Mapplets
  8. Mapping Parameters and Variables
  9. Working with User-Defined Functions
  10. Using the Debugger
  11. Viewing Data Lineage
  12. Comparing Objects
  13. Managing Business Components
  14. Creating Cubes and Dimensions
  15. Using the Mapping Wizards
  17. Datatype Reference
  18. Configure the Web Browser

Understanding Multi-Dimensional Metadata

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.

Updated June 25, 2018