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into the data warehouse The content and purpose of these ETL data stores is similar to those described for the bottom-up approach The characteristics of the data warehouse are: 1 What data will be stored here (reference and/or transaction data) Historical data, needed to satisfy business requirements, will be stored in the data warehouse This includes all historical transactions and history of all reference data that can be captured within the scope of what has been addressed to date 2 What is the primary purpose of keeping the data here The purpose is to facilitate the integration of data across the enterprise and to provide a single platform to store the data The data should support analytical and reporting needs of the organization The data warehouse serves as the central component of the enterprise solution All of the data is loaded here and then distributed as needed 3 How will the data be structured The data structures follow traditional, sound data design principles The data is organized into subject areas It is organized in a traditional third normal form The modeling technique for this data organization is called entity-relationship modeling (see 7) This data represents the pure data relationships, and when done properly eliminates redundancy 4 What is the persistence of the data or how much history will be stored In theory, all of the history that may be needed by the business is to be retained in the data warehouse All of the historical transactions are retained here The data warehouse also includes all changes to any reference data for the entire period of time that the transaction data is needed Data may be archived or deleted from the data warehouse when it is no longer needed by the business 5 Who will be able to use the data here Theoretically, business users may access the data in the data warehouse However, the complexity of the data structures can make it dif cult to write queries, and the data is not organized for optimal query performance It is not recommended that large numbers of users access the data warehouse structures directly Data that is needed by many people should be published, as described in the next layer There are probably a handful of highly sophisticated, data-savvy users with strong technical skills who are capable of accessing the data warehouse directly These are often market researchers, actuaries, or statisticians 6 What type of data access will they have The primary access to the data warehouse is through programs and utilities used to distribute data to the data marts Limited user access to these tables is possible, but not recommended for ad hoc querying.

sidwalk profile_file [profile_file ...] [/t] [/f [path]] [/r] [/s] [/p] [/g] [/l file] [/ ]

Both the bottom-up and top-down approaches utilize ETL data stores to ef ciently and accurately process the data However, the bottom-up approach does not include the data warehouse data storage as de ned here The primary data storage for a bottom-up approach is in the next layer

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Parameters profile_file /t /f [path] /r /s Explanation Specifies the path and filename of one or more .csv mapping files to be used for input. Conducts a test run. Scans all NTFS file system files or only those on the directory tree of the specified path. Scans the registry. Scans file shares.

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Table C-2 (continued)

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In order to more directly support each business area, a collection of the data needed for that speci c area can be loaded into a data mart Each data mart is intended to directly support the needs of a speci c business group It may be possible to de ne a semantic layer on top of the data warehouse to provide the data mart view If so, the data is not physically moved from the data warehouse The data architecture would re ect this here It is also common for data to be physically moved from the data warehouse into data marts This requires another layer of extraction, transformation, and loading (ETL) to move the data from the data warehouse to the data mart(s) The following describes the characteristics of this physical movement of data As noted in Figure 9-4, additional ETL data stores may be needed to support ETL processes to load data marts In some cases, the structures of the data warehouse itself may be modi ed to support the processes to load and maintain data marts Mastering Data Warehouse Design, by Claudia Imhoff, Nicholas Galemmo, and Jonathon Geiger (Wiley, 2003), is a good resource to learn more about these techniques 1 What data will be stored here (reference and/or transaction data) The data needed to support the speci c business analysis is to be loaded Theoretically, only summarized data is needed because the detailed transactions are in the data warehouse itself In reality, all frequently used transaction data is loaded into the data mart The reference data needed to support analysis is also loaded into the data mart 2 What is the primary purpose of keeping the data here The data marts are designed to support speci c business groups or analyses 3 How will the data be structured The data is usually stored in dimensional structures in the data mart The dimensional structures may be physically stored in a relational database or a multidimensional cube (this is a characteristic of the technical architecture) Sometimes data is stored in structures that are speci cally tailored to the business application For example, a at le may be created to support a speci c insurance catastrophe modeling software package If a semantic layer is used, rather than physically moving the data, then that should be noted here 4 What is the persistence of the data or how much history will be stored The historical data requirement varies according to the speci c business.

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Parameters /p /g /l file / (or with no switch) Explanation Scans printer shares. Scans local groups. Generates a Converter log file with file as the filename and path. Displays a usage screen.

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Security Migration Editor (Sidwalk.msc)

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