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Understanding Business Intelligence and Data Warehousing
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large warehouse, the fact table might easily have millions of rows using an integer key can substantially reduce the size of the fact table. The actual layout of a fact table might look more like that of the following sample rows:
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STATE_ID 1 1 2 2 1 PROD_ID 347 447 347 447 347 Month 1 1 1 1 2 Sales_Units 3 4 3 4 16 Sales_Dollars 7.95 7.32 7.95 7.32 42.40
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When you put integer keys into the fact table, the captions for the dimension members have to be put into a different table a dimension table. You will typically have a dimension table for each dimension represented in a fact table.
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Dimension Tables
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A dimension table contains the specific name of each member of the dimension. The name of the dimension member is called an attribute. For example, if you have three products in a Product dimension, the dimension table might look something like this:
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PROD_ID 347 339 447 Product_Name Mountain-100 Road-650 Cable Lock
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Product Name is an attribute of the product member. Because the Product ID in the dimension table matches the Product ID in the fact table, it is called the key attribute. Because there is one Product Name for each Product ID, the name is simply what you display instead of the number, so it is still considered to be part of the key attribute. In the data warehouse, the key attribute in a dimension table must contain a unique value for each member of the dimension. In relational database terms, this key attribute is called a primary key column. The primary key column of each dimension table corresponds to one of the key columns in any related fact tables. Each key value that appears once in the dimension table will appear multiple times in the fact table. For example, the Product ID 347, for Mountain100, should appear in only one dimension table row, but it will appear in many fact table rows. This is called a one-to-many relationship. In the fact table, a key column (which is on the many side of the one-to-many relationship) is called a foreign key column. The relational database uses the matching values from the primary key column (in the dimension table) and the foreign key column (in the fact table) to join a dimension table to a fact table.
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In addition to making the fact table smaller, moving the dimension information into a separate table has an additional advantage you can add additional information about each dimension member. For example, your dimension table might include the Category for each product, like this:
PROD_ID 347 339 447 Product_Name Mountain-100 Road-650 Cable Lock Category Bikes Bikes Accessories
Category is now an additional attribute of the Product. If you know the Product ID, you can determine not only the Product Name, but also the Category. The key attribute name will probably be unique because there is one name for each key, but other attributes don t have to be unique. The Category attribute, for example, may appear multiple times. This allows you to create reports that group the fact table information by Category as well as by product. A dimension table may have many attributes besides the name. Essentially, an attribute corresponds to a column in a dimension table. Here s an example of our small three-member Product dimension table with additional attributes:
PROD_ID 347 339 447 Product_Name Mountain-100 Road-650 Cable Lock Category Bikes Bikes Accessories Color Black Silver NA Size 44 48 NA Price 782.99 3,399.99 25.00
Dimension attributes can be either groupable or nongroupable. In other words, would you ever have a report in which you want to show the measure grouped by that attribute In our example, Category, Size, and Color are all groupable attributes. It is easy to imagine a report in which you group sales by color, by size, or by category. But Price is not likely to be a groupable attribute at least not by itself. You might have a different attribute say, Price Group that would be meaningful on a report, but Price by itself is too variable to be meaningful on a report. Likewise, a Product Description attribute would not be a meaningful grouping for a report. In a Customer dimension, City, Country, Gender, and Marital Status are all examples of attributes that would be meaningful to put on a report, but Street Address or Nickname are attributes that would most likely not be groupable. Nongroupable attributes are sometimes called member properties. Some groupable attributes can be combined to create a natural hierarchy. For example, if a Product key attribute has Category and Subcategory as attributes, in most cases, a single product would go into a single Subcategory, and a single Subcategory would go into a single Category. That would form a natural hierarchy. In a report, you might want to display Categories, and then allow a user to drill-down from the Category to the Subcategories, and finally to the Products.
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