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If there is no corresponding record with the same values for all type 2 attributes, a type 2 change has occurred Look at the sample data in the bottom of Figure 17-3 The third row of source data contains the SKU A-0033 This SKU is represented in the product table, but its brand_ manager has been changed This requires the insertion of a new row into the product dimension table A single record can contain both type 1 and type 2 changes In Figure 17-3, the fourth SKU to be processed, number A-1011, has undergone changes in product_name and brand_manager The product_name change is handled via a type 1 response, calling for the existing dimension row to be updated The new brand_manager is a type 2 change, which requires a new record to be inserted for the SKU This explains why step 16 is connected to step 1.

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7, and not to the end of the process flow TIP A source record can contain both type 1 and type 2 changes It is a grave error to consider this an either/or proposition Source records that do not contain any changes may pass all the way through this process without any impact on the dimension table The last record to be processed in Figure 17-3, SKU Z-3320, is already present in the dimension table (step 14), the corresponding record has the same values for the type 1 attributes (step 15), and also has the same values for all type 2 attributes (step 17) As you will see, performing changed data identification at the source can prevent having to perform these checks..

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In the Corporate Information Factory architecture, the enterprise data warehouse is not intended to be queried directly by analytic applications, business intelligence tools, or the like. Instead, its purpose is to feed additional data stores dedicated to a variety of analytic systems. The enterprise data warehouse is usually stored in a relational database management system, and Inmon advocates the use of third normal form database design. Surrounding the enterprise data warehouse are numerous other components. Of interest here are the data marts, which appear along the top of the diagram. These are databases that support a departmental view of information. With a subject area focus, each data mart takes information from the enterprise data warehouse and readies it for analysis. As the earlier quotation suggests, Inmon advocates the use of dimensional design for these data marts. The data marts may aggregate data from the atomic representation in the enterprise data warehouse. Note that Inmon reserves the term ETL for the movement of data from the operational systems into the enterprise data warehouse. He describes the movement of information from the enterprise data warehouse into data marts as data delivery. This book will use

the term ETL more generically, to describe any process that extracts data from one place and stores it somewhere else. The data marts serve as the focus for analytic activities, which may include queries, reports, and a number of other activities. These activities are enabled by a variety of different tools, including some that are commonly referred to as business intelligence tools and reporting tools. This book will collectively refer to these tools as business intelligence tools. Note, though, that Inmon reserves this term for a particular application in the Corporate Information Factory.

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