Star Schema

Star Schema

  • InfoCubes are made up of a number of InfoObjects. All InfoObjects (characteristics and key figures) are available independent of the InfoCube. Characteristics refer to master data with their attributes and text descriptions.   
  • An InfoCube consists of several InfoObjects and is structured according to the star schema. This means there is a large fact table that contains the key figures for the InfoCube, as well as several dimension tables which surround it. The characteristics of the InfoCube are stored in these dimensions.  
  • The dimensions and the fact table are linked to one another using abstract identification numbers (dimension IDs) which are contained in the key part of the particular database table. As a result, the key figures of the InfoCube relate to the characteristics of the dimension. The characteristics determine the granularity at which the key figures are stored in the InfoCube.  
  • Characteristics that logically belong together are grouped together in a dimension. Dimensions are to a large extent independent of each other, and dimension tables remain small with regards to data volume. This is beneficial in terms of performance as it decouples the master data from any specific InfoCube. The master data can be used at a time by multiple InfoCubes. This InfoCube structure is optimized for data analysis.  
  • The fact table and dimension tables are both relational database tables.  
  • Characteristics refer to the master data with their attributes and text descriptions. All InfoObjects (characteristics with their master data as well as key figures) are available for all InfoCubes, unlike dimensions, which represent the specific organizational form of characteristics in one InfoCube.  
  • You can create aggregates to access data quickly. Here, the InfoCube data is stored redundantly and in an aggregated form.  
  • You can either use an InfoCube directly as an InfoProvider for analysis and reporting, or use it with other InfoProviders as the basis of a MultiProvider or InfoSet.
Fact Table

  • The fact data are stored in a highly normalized fact table.
  • In a star schema, typically the fact table is very large with small dimensional tables.
  • The fact tables has a relatively small number of columns (attributes) and a large number of rows (records) where associated dimension tables to have a large number of columns (attributes) and small number of rows.
Dimension Table

  • Dimension data are stored in dimension table.
  • Dimension table link to the fact table has a group of similar characteristics. For example, a customer dimension table may contain three characteristics: customer name, address and sales organization. There will be one customer dimension record for each unique combination of these three values.  For example, each record in customer dimension may represent a specific customer.


Limitations of Star Schema


  • In Case of star schema, Master data is stored inside the cube. So Master data cannot be reused in other cubes. 
  • Since all the tables inside the cube contains Alpha-numeric data, it degrades query performance. Because processing of numeric’s is much faster than processing of alphanumeric.
  • In case of Star schema, we are limited to only 16 dimensions.


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