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Printing Quick Response Code in Software More Slow Change Techniques

CHAPTER
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More Slow Change Techniques
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A crucial part of star schema design is determining how changes to source data will be reflected in dimension tables. The change response pattern for each dimension attribute in the star schema must be carefully matched to business requirements. Most requirements can be satisfied by employing the techniques introduced in 3: Type 1 is used when the history of the data element is not significant. When a source data element changes, the corresponding dimension attribute is overwritten. This causes previously recorded facts to become associated with the changed value. No trace of the old value is left behind. Type 2 is used when it is important to preserve the historic context of facts with respect to the changing data element. When the source data element changes, a new row is added to the dimension table, leaving the previous version unchanged. Previously recorded facts remain associated with the old version; any new facts are associated with the changed version. This chapter describes situations where these options do not suffice. It is divided into sections for three additional techniques: Time-stamped dimensions are used when it is necessary to support point-in-time analysis of dimension values, irrespective of associated facts. Type 3 changes are employed when users want to study all the facts recorded both before and after the data element changes using either the new value or the old value. Hybrid techniques are employed when requirements conflict, calling for more than one kind of response. People developing queries and reports will need to know how to exploit these solutions effectively. These techniques also place increased demands on the ETL process. It is, therefore, important to choose wisely, and that requires a thorough understanding of each option.
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PART III
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Dimension Design
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The most common response to changes in source data is the type 2 slowly changing dimension. If there is any uncertainty about requirements for historic data, 3 advised designers to choose a type 2 response. It is the safe choice because it preserves the association of historic dimension values with facts that have been recorded in fact tables. No information is discarded. The type 2 response has one glaring shortcoming: it cannot tell you what the dimension looked like at any point in time. This is a particular concern if you have a dimensional data warehouse architecture or stand-alone data mart. In these architectures, the dimensional model doubles as the integrated repository of granular data. In a Corporate Information Factory, maintaining a full history in the dimensional data mart is a lesser concern since there is also an enterprise data warehouse repository to hold it. Although not configured for direct access, at least the information is not thrown away. An additional fact table can come to the rescue and be used to track the history of changes to the dimension. This fact table has the odd characteristic of having exactly the same number of rows as the dimension table, but it does the job. Many designers instinctively gravitate to a more flexible alternative: supplementing the type 2 response with time stamps. The time-stamped dimension permits three forms of point-in-time analysis within the dimension table itself: Easily order a chronological history of changes Quickly select dimension rows that were in effect for a particular date Easily identify the dimension rows currently in effect The time-stamped dimension has an unusual property. Joined to a fact table, it behaves like any other dimension table. Used on its own, it also exhibits some of the characteristics of a fact table. The time-stamped approach can also be tremendously useful to ETL developers charged with loading data into fact tables.
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Point-in-Time Status of a Dimension
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Often, one or more dimension tables in the data warehouse represent closely watched entities. The history of attribute values is significant and is often monitored irrespective of any associated transactions. Documents, contracts, customers, and even employees may be subjected to this deep scrutiny. When it is necessary to support point-in-time analysis within a dimension table, type 2 changes alone will not do the job.
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