N O T E Don t ignore topics that are discussed in terms of the entire enterprise in .NET framework

Generating Quick Response Code in .NET framework N O T E Don t ignore topics that are discussed in terms of the entire enterprise
N O T E Don t ignore topics that are discussed in terms of the entire enterprise
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Too often this is assumed to be applicable only to very large multi-divisional
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9
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Architecture, Infrastructure, and Tools
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companies Enterprise means across your entire company, regardless of the size Similar problems have been observed for organizations of any size, from single line of business to global conglomerate
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Data architecture provides the umbrella to help an organization meet these overarching data warehouse goals It is time to look more closely at the different parts of the data warehouse data architecture Keep in mind that the speci c business and systems requirements of each organization must be taken into consideration when developing the data architecture
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Components of DW Data Architecture
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Any architecture can seem daunting to understand, but it does not need to be As shown in Figure 9-2, the overall ow of data in a data warehouse consists of several basic layers, which need to be de ned by the data architecture
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Capture/Create the Data
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Extract the Data
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Prepare the Data
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Publish the Data
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Use the Data
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Figure 9-2 Layers of data architecture data ow
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The rst layer of the data architecture is for capturing or creating the data This is not within the realm of the data warehouse, but is done by the source systems The speci c architecture of the source systems is determined independently of the data warehouse, but this is where you begin The second layer is the process to get the data from wherever it currently lives in the organization This may be a simple extract from a database or a complex set of programs to pull the necessary data, or a utility to pull the data from a third-party application such as a nancial system The third layer is to prepare
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Part IV
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Building the Project
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the data for reporting and analytical use This is the where most of the work for a data warehouse is done including validation, cleansing, and integration The fourth step of data architecture is to publish the data that is ready for access Here, data is made available for reporting and analysis The nal step is to begin using the data itself At each layer, several basic things need to be de ned regarding the data: 1 What data will be stored here (reference and/or transaction data) 2 What is the primary purpose of keeping the data here 3 How will the data be structured 4 What is the persistence of the data or how much history will be stored If it is no longer needed, how is this handled 5 Who will be able to use the data here 6 What type of data access will they have There are many different alternatives for a data warehouse data architecture A complete data architecture de nes parameters and guidelines for the preceding questions for each of the layers illustrated previously in Figure 9-2 A review of the most widely adopted data architectures follows in the next section
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A Closer Look at Common Data Warehouse Architectures
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While there are many philosophical approaches to data warehouse architecture, two are by far the most widely adopted: the bottom-up approach and the top-down approach These were introduced as Kimball/Inmon in 1, and each is explored in more detail here Both are viable alternatives for implementing your data warehousing environment It is important to understand the basic principles of each in order to select the approach that will work the best for your organization Having a good grasp of the most prevalent data architectures can improve your understanding of any other approaches that your organization may consider or choose to adopt These data architectures are each de ned within the context of an overall data warehouse methodology The data architectures described next are pulled from each methodology
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Bottom-Up Data Architecture
The bottom-up approach has been well documented by Ralph Kimball and his colleagues in his Data Warehouse Toolkit series of texts This approach has a strong focus on the end user and the delivery of value to the business The goal is to ensure support across the enterprise for consistent reporting and analysis