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Part I
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PART I
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In this book, surrogate keys will be easily identifiable by the suffix _key in the column name. In Figure 1-5, for example, the surrogate key for the customer dimension is called customer_key, the surrogate key for the salesperson dimension is called salesperson_key, and so forth. Illustrations in this book will always list the surrogate key for a dimension table as its first attribute. Dimension tables also contain key columns that uniquely identify something in an operational system. Examples in Figure 1-5 include customer_id, sku, and salesperson_id. In the operational systems, these columns identify specific customers, products, and salespeople, respectively. These key columns are referred to as natural keys. The separation of surrogate keys and natural keys allows the data warehouse to track changes, even if the originating operational system does not. For example, suppose that customer ABC Wholesalers is identified by customer_id 10711 in an operational system. If the customer changes its headquarters location, the operational system may simply overwrite the address for customer_id 10711. For analytic purposes, however, it may be useful to track the history of ABC Wholesalers. Since the star schema does not rely on customer_id to identify a unique row in the customer dimension, it is possible to store multiple versions of ABC Wholesalers, even though both have the same customer_id 10711. The two versions can be distinguished by different surrogate key values. While it would also be possible to support change tracking by supplementing a natural key with a sequence number, the surrogate key allows fact and dimension tables to be joined based on a single column. The term slowly changing dimension refers to the manner in which a dimensional schema responds to changes in a source system. Detailed examples will be explored in 3, Stars and Cubes. Advanced techniques are explored in 8, More Slow Change Techniques.
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Fact Tables
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At the core of a star schema is the fact table. In addition to presenting the facts, the fact table includes surrogate keys that refer to each of the associated dimension tables. The simple orders star in Figure 1-5, for example, includes the facts quantity_ordered, cost_dollars, and order_dollars. It also includes surrogate keys that refer to products, salespeople, customers, and order dates. Together, the foreign keys in a fact table are sometimes considered to identify a unique row in the fact table. This is certainly true in Figure 1-5, where each fact table row represents orders of a product sold by a salesperson to a customer on a given day. In other cases, however, the foreign keys in a fact table are not sufficient to identify a unique row. As we will see in 3, sometimes a fact table row can be uniquely identified by a subset of its foreign keys, or even by using some nonkey attributes. Each row in the fact table stores facts at a specific level of detail. This level of detail is known as the fact table s grain, a term that will be explored in 3. The information held in fact tables may be consumed at a variety of different levels, however, by aggregating the facts. In some data warehouse architectures, it is critical that the star schema capture information at the lowest level of detail possible. In other architectures, this is less important because a separate part of the data warehouse architecture is reserved for atomic data. These variations in data warehouse architecture are explored in 2, Data Warehouse Architectures.
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Using a Star Schema
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There is still much to be said about the fundamentals of dimensional design, a discussion that continues in 3, but enough background has now been provided to look at an
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