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A relational warehouse is often called a star schema, simply because of the look of the schema In the middle sits a fact table with one or more measures The fact table has a join back to each dimension table, which often surround the fact table in diagrams The practice of placing the fact table at the center of the schema diagram is merely tradition, but it helps those familiar with relational warehousing quickly grasp the structure of the warehouse In some cases a dimension table is normalized somewhat, so that a Product dimension is broken into Product, Product Category, and Product Subcategory tables This structure is called a snowflake schema because the dimension tables branch out, much like the ice crystals of a snowflake Figure 3-4 shows a simple star schema In many data warehouses, there are multiple fact tables This may be due to several factors, two of which are relatively common First, different fact tables may contain different dimensions A fact table that supports manufacturing will have a product dimension and an assembly line dimension A fact table that supports sales will have a product dimension and a customer dimension Assembly line has nothing to do with
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Figure 3-4
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A simple star schema showing the fact table and related dimension tables
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3: Data Warehousing and Business Intelligence
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sales, and the customer has nothing to do with manufacturing Note that these fact tables should, however, have a conformed product dimension table joined to them Second, fact tables may be at differently levels of granularity Fact tables that contain actual data are typically at lower levels of detail, such as Day Fact tables designed to hold forecast or budget values are often at higher levels of detail, such as Month or Quarter Therefore, while both the sales and forecast fact tables contain a Time dimension, they have a different grain and therefore cannot be a single fact table One aspect of fact tables that breaks with traditional normalization techniques is that calculated fields are often included as some of the measures Typically, calculated fields are not stored in a relational table, but there is nothing wrong with storing calculated fields in a fact table In some cases this can improve performance by calculating the values ahead of time instead of requiring the cube engine to calculate the values on the fly Calculations in the cube engine will be mentioned later in this chapter
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Extracting, Transforming, and Loading Data
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After designing the relational data warehouse to support the needs of the business, the next step is to identify the data sources, extract the data, transform data as needed, and then load the data into the relational warehouse This process is generically called extraction, transformation, and loading, or ETL There are many ETL tools available and while most can connect to all the common data sources, the real differences lie in their abilities to control the flow of data and provide transformations to the data Data transformation is required because data stored in disparate source systems may be stored in different formats, with different codes, or using different data types For example, one system might store a Yes as 1 and a No as 0, while another system may store the strings Y and N If data is pulled from two systems and a Yes/No field is needed, then the data must be transformed so that it is consistent in the warehouse Data consistency is one of the cornerstones of a data warehouse; all data must be in the same format for analysis to be effective Other issues arise when considering items such as currencies In a global business, there are often source systems in each country recording receivables, and normally the business systems store these receivables in the local currency If a business has branches in London, Paris, Tokyo, and New York, it s likely that data is stored in Pounds, Euros, Yen, and US Dollars In building the warehouse, sales measured in Yen cannot simply be added to sales in US Dollars in order to get non-European sales Instead, the data must be transformed by applying some form of currency conversion to the data before storing it in the warehouse Whether this is done by
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