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So far, we ve seen how control flow components get things done in package, but the real power of Integration Services is the data flow engine. Now we re ready to examine the components used by the data flow engine. To work with data flow components, you first add a Data Flow Task to the control flow, then define one or more sources as the beginning of one or pipelines within the data flow. A single Data Flow Task could manage multiple pipelines if the order of operations doesn t matter. Each pipeline will run in parallel, more or less. (The degree of parallelism depends on system resources and the data volumes in each pipeline). Otherwise, you ll need to create multiple Data Flow Tasks and use precedence constraints to manage sequencing. It s helpful to think of the data flow pipeline as a physical pipeline with discrete start and end points and with individual channels running through this pipeline, as shown in Figure 21-11. Each channel corresponds to a column in the row of data currently in



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the pipeline. As columns are removed during transformations, the number of channels in the pipeline is reduced downstream. Conversely, as new derived columns are added to the pipeline, the number of channels downstream is increased. Both the data type and the size of the data traveling through a particular channel must stay constant unless a transformation like Data Conversion modifies the channel metadata. While the pipeline is a useful metaphor, in actuality the data is placed in a buffer and transformed in place; only certain transformations physically move data from one buffer to another.

The isValid() method automatically verifies CAPTCHA and hash values, with no additional programming required on your part

Input Transformation Output columns Output Error output Input columns Destination Input columns Input Destination Error output Input columns Destination

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Every data flow path starts with a source and usually ends with a destination. Data sources and data destinations are data flow components that sit between the physical connection to the data storage and the buffers created in memory to transport the data to another location. In most cases, the type of data storage accessed as a source can also be accessed as a destination. The direction the data flows out of or into this type of object determines whether it is a data source or a data destination. There are, however, some special data destinations for which there is no corresponding data source. As you work with these objects, you ll notice that a data source has two outputs, one for normal data flow and one for error output. By contrast, a data destination has only one input and one error output. Table 21-1 lists the available data adapters and their use as source, destination, or both.

Table 21-1 Available Data Adapters and Their Uses Data Adapter Data Mining Model Training DataReader Dimension Processing Excel Flat File OLE DB Partition Processing Raw File Recordset Script Component SQL Server SQL Server Mobile XML Source Destination

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If you re already responsible for accessing data in a variety of structures, you ll likely have no trouble applying what you know to the configuration of data sources and data destinations. Some of these objects, however, warrant further explanation:

Data Mining Model Training You can automate the process of training an Analysis Services data mining model by using this destination. Before using this destination, be sure you understand the content and structural requirements of the data mining model.

DataReader As a destination, the DataReader destination can expose the data in the pipeline to applications than can access the ADO.NET DataReader interface. Using this approach, for example, you can load data directly into a Reporting Services report without persisting the data in a file or relational table. Dimension Processing Use this destination to automate updating dimension objects in Analysis Services databases. Dimension processing is explained in 22, Analysis Services. Partition Processing This destination is used to load fact table data into an Analysis Services partition, which is also explained in 22. SQL Server This destination type can be used only if the package executes on the same server hosting the target database in SQL Server 2005. Because this destination actually sends data to the database directly from memory without requiring a connection, performance can be up to 25 percent faster than using an OLE DB connection. Note

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The next listing illustrates how these methods are typically used in the context of a controller script:

If you re loading data to a SQL Server destination on another server, use the OLE DB destination. In the OLE DB Destination Editor, select Table Or View Fast Load or select Table Name Or View Name Variable Fast Load in the Data Access Mode drop-down list to get the best performance.

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