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The population of the water-analogy-filled world of the Data Flow is about to increase by two. These new watery terms are a necessary addition to your SSIS vocabulary: Upstream: This term refers to a component or transformation that exists before the current point in the Data Flow task. Downstream: This refers to a component or transformation that exists after the current point in the Data Flow task. Armed with an ability to describe the flow of data as it cascades through your Data Flow task, you re about to embark on a voyage further into the Data Flow architecture. All aboard the good ship SSIS!

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Listing 8-4. Importing Namespaces using using using using System.Collections; System.Diagnostics; System.Web.Services.Protocols; SSRS_Viewer_RVC.SSRSWebService;

You can also use the peruser overrides for the password policy, for example, to make sure that the vice presidents or similar account holders with access to much sensitive information change their passwords more frequently and have minimum character lengths of 12 instead of 8 Again, all users with administrative privileges are exempt from the password policy, so it is essential to ensure you use a secure password for those accounts and practice your own diligence in rotating them..

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1 input, 2 outputs The Percentage Sampling transform generates and outputs a dataset into the Data Flow based on a sample of data. The sample is entirely random to represent a valid cross-section of available data. As the task name suggests, the number of rows in the generated dataset is a set percentage of the source-input rows. It s worth noting that the number of rows returned will rarely be precisely equal to the specified percentage of rows required. Don t even give this another moment of thought. It is unimportant. Because the generated dataset is representative of the full set of data, using this transform to produce a sample is useful in a variety of ways. I would go so far as to say that producing these subsets of data is invaluable in environments with large volumes of source data. The task is also often used for training and testing data-mining models. The two properties that have a real bearing on how this task performs are configurable from the editor screen (Figure 9-38). The Percentage property is self-explanatory, but the random-seed value gives access to an interesting feature.

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In environments with more than one administrator, or when the organization is large enough to require delegation of duties, using tiered administration means that specific groups of users can have access to modify some aspects of the Open Directory system but not the entire situation You can create limited administrator profiles by select an existing user in Workgroup Manager and choosing the Privileges tab By setting the configuration options under the Limited choice in the drop-down menu, you can specify what groups or users specific limited administrators can modify and what modifications they are allowed to make As you can see, there is no way to create limited administrators who can also create users, since that would grant too much access (in theory, they could create users with more privileges than themselves and then log in as those users).

Next, add the class variables shown in Listing 8-5 to PickSchedule.cs just below the class declaration. The ReportingService2005 type contains the methods and properties you can use to call the SSRS 2005 Report Server Web service and is made available through the Web reference you added to the SSRS Viewer RVC project in 6. Listing 8-5. Class-Level Private Variables private private private private string url; string server; string report; ReportingService2005 rs;

Figure 9-38. Few properties, but they re perfectly formed The method by which random rows are selected for the data sample is based on a seed value and is therefore only pseudorandom. The seed value in general task operations is gathered from the operating system, in this case uptime expressed as a number of ticks. This generates a pseudorandom number instead of a truly random one because using the same value as the seed gives the same set of random numbers. This method of creating pseudorandom numbers has gone unchanged since the early days of computing.

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