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In a production database, it is critical for changing values to be consistent across the entire application: If you change a customer s address in one part of the system, you want the changed address to be immediately visible in all parts of the system. Because of this need for consistency, production databases tend to be broken up into many tables so that any value is stored only once, with links (or joins) to any other places it may be used. Ensuring that a value is stored in only one place is called normalization, and it is very important in production database systems. In a data warehouse dimension, you may have multiple attributes that form a natural hierarchy. For example, several products might belong to a subcategory, and several subcategories are grouped into a category. A database designer who is familiar with creating production databases will want to normalize the dimension so that there is a separate Subcategory table where each subcategory appears only once, and then a separate Category table where each category appears only once. This, of course, requires foreign keys in the Product and Subcategory tables that join to unique primary keys in the Subcategory and Category tables, respectively.
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If you are creating reports against the data warehouse, however, many joins can make the query slow. For example, if you want to see the total sales for the Bikes Category for the year 2006, you would have to join each row in the fact table to the Product table, and then to the Subcategory table, and then the Category table, and also to the Date table, the Month table, then the Quarter table, and finally to the Year table. And you would have to do all those joins to all the rows in the fact table, just to find out which ones to discard. This makes the query for a relational report much slower than it needs to be. The fact is that values in a data warehouse are not changing as dynamically as they would in a production database, so storing the values redundantly is less important than is retrieving the values as quickly as possible for a report. Consequently, in many data warehouses, all the attributes for a dimension are stored in a single dimension table even if that means that categories and years are stored redundantly many times. Storing redundant values in a single table is called denormalizing the data. The concept is that dimension tables are relatively small (compared to the fact tables), and that performing a single join to find out the Year and the Category is much faster with only a couple of joins, so denormalizing is worth doing. Storing all the attributes for each dimension in a single denormalized dimension table produces what is called a star schema, because you end up with a single fact table surrounded by a single table for each dimension, and the result looks a bit like a star. Normalizing each of the dimension tables so that there are many joins for each dimension results in a snowflake schema, because the points of the star get broken up into little branches that look like a snowflake. In reality, it isn t the database that is star or snowflake, because one dimension might be fully normalized (i.e., a snowflake), while another dimension in the same data warehouse might be fully denormalized (i.e., a star). In fact, even within a single dimension, some attributes might be normalized into a snowflake while others are denormalized into a star. If you are creating a data warehouse for the purpose of creating reports directly from a relational database, the more snowflaking you do with attributes, the slower the query that populates the report will run. If, however, you will use the warehouse primarily as a data source for Analysis Services, then the difference between star or snowflake dimension attributes is much less significant, and you can use other reasons (such as which database structure is easier to create and update) as the basis for a design decision.
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