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80 Part I EXAM PREPARATION TIPEXAM Normalization on the Exam The exam will require that you know what to denormalize, as stated in the Microsoft exam sub-objective: Specify degree of normalization. Normalization is not always the best design for a given database. Normalization creates numerous, small, interrelated tables. Processing the data in these tables can incur a great deal of extra work and other overhead to combine the related data. The extra processing reduces the performance of the database. In these situations, denormalizing the database slightly to simplify complex processes can improve performance. transaction processing) may not have redundant updates and may be more understandable and efficient for queries if the design is not fully normalized. In data warehousing the results of calculations are often stored with the data, so that type of processing does not have to occur when the data is read. Many reporting systems also denormalize data to produce results specific to the application. Sometimes you ll encounter situations where a fully normalized data model just won t perform in the situation you place it in. In situations like this, you have to denormalize your database. A normalized database needs more join queries to gather information from multiple entities (because entities are divided into smaller entities when undergoing the process of normalization). Therefore, CPU usage might overwhelmingly increase, and cause an application to slow or freeze. In situations like this, denormalization is appropriate. Normalization and denormalization processes begin to put a high- performance database system together in a logical fashion. Any seasoned database person knows, however, that performance isn t the only concern. It is necessary in any database system to minimize the administrative management needed to keep a database functional and accurate. The aim here is integrity. MAINTAINING DATA INTEGRITY . Design attribute domain integrity. Considerations include CHECK constraints, data types, and nullability. Specify scale and precision of allowable values for each attribute. Allow or prohibit NULL for each attribute. Specify allowable values for each attribute. Whether you have implemented or are in the process of implementing a data model, you will need to keep data integrity in mind as a key factor in verifying the correctness and uniqueness of data. Data integrity itself means preserving the correctness and verifying the consistency of data. When incorrect or inconsistent values and

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