TAILIEUCHUNG - SAS Data Integration Studio 3.3- P23

SAS Data Integration Studio P23:This manual is a companion to the online Help for SAS Data Integration Studio. The Help describes all windows in SAS Data Integration Studio, and it summarizes the main tasks that you can perform with the software. The Help includes examples for all source designer wizards, all target designer wizards, and all transformation templates in the Process Library tree. | Main Tasks for Users A Updating Metadata for Jobs 105 Create Match Code and Apply Lookup Standardization Transformations The Process Library tree includes two transformation templates that require SAS Data Quality Server software Create Match Code and Apply Lookup Standardization. These transformations enable you to increase the value of your data through data analysis and data cleansing. To use these transformations the SAS Data Quality Server software must be installed a SAS application server must be configured to access a Quality Knowledge Base and the Quality Knowledge Base must contain the locales that you need to reference in your SAS Data Integration Studio jobs. When the prerequisites have been met you can drag and drop these transformations into your process flow diagrams. SAS Data Quality Functions in the Expression Builder Window SAS Data Integration Studio provides an Expression Builder window in the properties window of some transformations. For a description of this window see Expression Builder Window on page 16. If SAS Data Quality Server software is available to you the Expression Builder window includes a wide range of data quality functions. One way to see the data quality functions is to open the properties window of the SQL Join transformation and select the Where tab. For detailed information about the data quality functions see the SAS Data Quality Server Reference which is available in the online SAS Help and Documentation for Base SAS and in SAS OnlineDoc. Data Validation Transformation When incorporated into SAS Data Integration Studio jobs the Data Validation transformation enables you to detect error conditions and specify responses to those errors. Error conditions include blank or missing values duplicate values and invalid values. The actions that you can take in response to erroneous values include stopping the job changing the value or writing the row to an error table instead of to the target. The Data Validation transformation .

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