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STAR Schema Design

Oracle Tips by Burleson Consulting

The Data Warehouse Development Life Cycle

Oracle Data Warehouse Design

Massive De-normalization: STAR Schema Design

Note the dimension tables surrounding the fact table. Some of the dimension tables contain data that can be added to queries with joins, while other dimensions such as REGION do not contain any data, and only serve as indexes to the data.

Considering the huge disk space consumption and read-only restriction, what does this STAR schema really buy for us? The greatest benefit of the STAR schema is the simplicity of data retrieval. Now that we have a STAR schema, we can formulate SQL queries to quickly get the information we desire. For example, we can use the following simple query to get the total cost for an order :

SELECT sum(total_cost) order_total

By doing some of the work up front, the realtime query becomes both faster and simpler.

Now, let's consider what would happen if the user of this schema wanted to analyze information by aggregate values. Assume our manager wants to know the breakdown of sales by region. The data is not organized by region, but the fact table can easily be queried to find the answer.

At this point, retrieving the sum of all orders for the Western region becomes trivial, as shown in the following snippet:

SELECT sum(total_cost)

In addition to making the query simpler in structure, all of the table joining has been eliminated, so we can easily get the extracted information from our STAR schema.

Note:  A value such as REGION would be an ideal candidate for the use of Oracle7.3 bitmapped indexes. Columns that have a small number of  distinct values can see dramatic performance improvements by utilizing the bitmapped index technique. Bitmapped indexes are described in detail in Chapter 8, Oracle Features For The Data Warehouse.

The natural consequence of this approach is that many IS shops will keep two copies of their production databases: one in third normal form for online transaction processing and another de-normalized version of the database for decision support and data warehouse applications.

This is an excerpt from "High Performance Data Warehousing", copyright 1997.
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