Inside the Data Flow, destinations accept the data from the Data Sources and from the transformations. The architecture can send the data to nearly any OLE DB–compliant Data Source, a flat-file, or Analysis Services, to name just a few. Like sources, destinations are managed through Connection Managers. The configuration difference between sources and destinations is that in destinations, you have a Mappings page, where you specify how the inputted data from the Data Flow map to the destination. As shown in the Mappings page in this figure, the columns are automatically mapped based on column names, but they don’t necessarily have to be exactly lined up. You can also choose to ignore given columns, such as when you’re inserting into a table that has an identity column, and you don’t want to inherit the value from the source table.
In SQL Server 2014, you can start by configuring the destination first, but it would lack the metadata you need. So, you will really want to connect to a Data Flow path. To do this, select the source or a transformation and drag the blue arrow to the destination. If you want to output bad data or data that has had an error to a destination, you would drag the red arrow to that destination. If you try to configure the destination before attaching it to the transformation or source, you will see the error in Figure below. In SQL Server 2014, you can still proceed and edit the component, but it won’t be as meaningful without the live metadata.
The Excel Destination is identical to the Excel Source except that it accepts data rather than sends data. To use it, first select the Excel Connection Manager from the Connection Manager page, and then specify the worksheet into which you wish to load data.
WARNING The big caveat with the Excel Destination is that unlike the Flat File Destination, an Excel spreadsheet must already exist with the sheet into which you wish to copy data. If the spreadsheet doesn’t exist, you will receive an error. To work around this issue, you can create a blank spreadsheet to use as your template, and then use the File System Task to copy the file over.
The commonly used Flat File Destination sends data to a flat file, and it can be fixed-width or delimited based on the Connection Manager. The destination uses a Flat File Connection Manager. You can also add a custom header to the file by typing it into the Header option in the Connection Manager page. Lastly, you can specify on this page that the destination file should be overwritten each time the Data Flow is run.
Your most commonly used destination will probably be the OLE DB Destination (see Figure 4-11). It can write data from the source or transformation to OLE DB–compliant Data Sources such as Oracle, DB2, Access, and SQL Server. It is configured like any other destination and source, using OLE DB Connection Managers. A dynamic option it has is the Data Access Mode. If you select Table or View – Fast Load, or its variable equivalent, several options will be available, such as Table Lock. This Fast Load option is available only for SQL Server database instances and turns on a bulk load option in SQL Server instead of a row-by-row operation.
A few options of note here are Rows Per Batch, which specifies how many rows are in each batch sent to the destination, and Maximum Insert Commit Size, which specifies how large the batch size will be prior to issuing a commit statement. The Table Lock option places a lock on the destination table to speed up the load. As you can imagine, this causes grief for your users if they are trying to read from the table at the same time. Another important option is Keep Identity, which enables you to insert into a column that has the identity property set on it. Generally speaking, you can improve performance by setting Max Insert Commit Size to a number like 10,000, but that number will vary according to column width.
New users commonly ask what the difference is between the fast load and the normal load (table or view option) for the OLE DB Destination. The Fast Load option specifies that SSIS will load data in bulk into the OLE DB Destination’s target table. Because this is a bulk operation, error handling via redirection or ignoring data errors is not allowed. If you require this level of error handling, you need to turn off bulk loading of the data by selecting Table or View for the Data Access Mode option. Doing so will allow you to redirect your errors down the red line, but it causes a slowdown of the load by a factor of at least four.
The Raw File Destination is an especially speedy Data Destination that does not use a Connection Manager to configure. Instead, you point to the file on the server in the editor. This destination is written to typically as an intermediate point for partially transformed data. Once written to, other packages can read the data in by using the Raw File Source. The file is written in native format, so it is very fast.
The Recordset Destination populates an ADO recordset that can be used outside the transformation. For example, you can populate the ADO recordset, and then a Script Task could read that recordset by reading a variable later in the Control Flow. This type of destination does not support an error output like some of the other destinations.
The Data Mining Model Training Destination can train (the process of a data mining algorithm learning the data) an Analysis Services data mining model by passing it data from the Data Flow. You can train multiple mining models from a single destination and Data Flow. To use this destination, you select an Analysis Services Connection Manager and the mining model. Analysis Services mining models are beyond the scope of this Tutorial; for more information.
NOTE The data you pass into the Data Mining Model Training Destination must be presorted. To do this, you use the Sort Transformation.
The DataReader Destination provides a way to extend SSIS Data Flows to external packages or programs that can use the DataReader interface, such as a .NET application. When you configure this destination, ensure that its name is something that’s easy to recognize later in your program, because you will be calling that name later. After you have configured the name and basic properties, check the columns you’d like outputted to the destination in the Input Columns tab.
The Dimension Processing Destination loads and processes an Analysis Services dimension. You have the option to perform full, incremental, or update processing. To configure the destination, select the Analysis Services Connection Manager that contains the dimension you would like to process on the Connection Manager page of the Dimension Processing Destination Editor. You will then see a list of dimensions and fact tables in the box. Select the dimension you want to load and process, and from the Mappings page, map the data from the Data Flow to the selected dimension. Lastly, you can configure how you would like to handle errors, such as unknown keys, in the Advanced page. Generally, the default options are fine for this page unless you have special error-handling needs. The Partition Processing Destination has identical options, but it processes an Analysis Services partition instead of a dimension.
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Yamuna Karumuri is a content writer at Mindmajix.com. Her passion lies in writing articles on IT platforms including Machine learning, PowerShell, DevOps, Data Science, Artificial Intelligence, Selenium, MSBI, and so on. You can connect with her via LinkedIn.
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