Guide to Paradox 386 by Bradley Dyck Kliewer

By Bradley Dyck Kliewer

Part of the preferred Database specialist sequence, advisor to Paradox 386 is written for basic programmers, software program builders, software programmers with database adventure. specializes in the advance of large-scale databases on IBM PC's or compatibles utilizing Borland's Powerhill Paradox 386 database.

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At present, data mining is not supported by the tabular model data and therefore we will be using the multidimensional project for our example, shown in the following screenshot: [ 41 ] Data Model Preparation and Deployment We now fill the following details: • Name: TestDataMining • Location: D:\DataMining\TestDataMining (you can choose a drive of your choice) • Solution name: TestDataMining Then, select the checkbox for Create directory for solution and click on OK. We now add a data source to the project by clicking on Data Source and then clicking on New, as shown here: [ 42 ] Chapter 2 The list of available providers that the data source supports is shown here: We click on New and then make the selection, as shown: In the next screen, we select the Use the service account option and name the data source in the next screen as TestDataMining and then click on Finish.

As it is clear, ETL has the following three parts: • Extract: The data is extracted from a different source database and other databases that might contain the information that we seek • Transform: Some transformation is applied to the data to fit the operational needs, such as cleaning, calculation, removing duplicates, reformatting, and so on • Load: The transformed data is loaded into the destination data store database [ 16 ] Chapter 1 We usually believe that the ETL is only required till we load the data onto the data warehouse but this is not true.

We will now get more specific on the meaning of the benchmark data or the set of conditions. The following are some of the situations that would help us to get a deeper understanding of what criteria a benchmark data will possess: • The data will have a very high conformance to the constraints or rules of the data store such as the target data type, the target data ranges, the target foreign key constraints, the target unique constraints, and so on. • The data should not be incomplete, for example, some of the data or group of data should together add up to a fixed total.

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