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In Depth Information
Figure 1-6. An OLAP multidimensional cube showing sales
Now imagine cubes with multiple facts and dimensions, each dimension having its own hierarchies across each
cube, and all these cubes connected together. The information residing inside this consolidated cube can deliver very
useful, accurate, and aggregated information. You can drill down into the data of this cube to the lowest levels.
However, earlier we said that OLAP databases are denormalized. Well then, what happens to the tables? Are they
not connected at all and just work independently?
Clearly, you must have the details of how your original tables are connected. If you want to convert your
normalized OLTP tables into denormalized OLAP tables, you need to understand your existing tables and their
normalized form in order to design the new mapping for these tables against the OLAP database tables you’re
planning to create.
To plan for migrating OLTP to OLAP, you need to understand OLAP internals. OLAP structures its tables in its
own style, yielding tables that are much cleaner and simpler. However, it’s actually the data that makes the tables
clean and simple. To enable this simplicity, the tables are formed into a structure (or pattern) that can be depicted
visually as a star. Let’s take a look at how this so-called star schema is formed and at the integral parts that make up
the OLAP star schema.
Facts and Dimensions
OLAP data tables are arranged to form a star. Star schemas have two core concepts: facts and dimensions. Facts are
values or calculations based on the data. They might be just numeric values. Here are some examples of facts:
Dell US Eastern Region Sales on Dec. 08, 2007 are $1.7 million.
Dell US Northern Region Sales on Dec. 08, 2007 are $1.1 million.
Average daily commuters in Loudoun County Transit in Jan. 2010 are 11,500.
Average daily commuters in Loudoun County Transit in Feb. 2010 are 12,710.
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