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A Data Flow task represents an instance of the SSIS data flow engine executable. This executable is responsible for calling the data sources, data destinations, and any transformations that have been added to the Data Flow task. As you saw in our first example in this chapter, when you add the Data Flow task to the Control Flow design surface and double-click it, you are directed to the Data Flow design surface. A Data Flow task can contain one or more Data Flow tasks (which we ll review in the Data Flow Designer Tasks section later in this chapter). You can also have more than one Data Flow task on the Control Flow design surface. As noted earlier, the Data Flow tasks describe how data moves from a data source (source adapter), and how the data is modified and mapped (transformation) to a destination (destination adapter). In other words, you take data from somewhere, change it in some way, and then store the results somewhere else.

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Optional features of pie charts include the following: Annotations for the data to be displayed with each slice Drill-through capability (clicking on a slice displays details data) A grid with the source data set (in addition to or in lieu of a legend)

Listing 3-10. Recipe 3.11 MainPage.Xaml.cs Class File using System.Windows.Controls; using System.Windows.Input; namespace Ch03_DevelopingUX.Recipe3_11 { public partial class MainPage : UserControl { public MainPage() { InitializeComponent(); } private void Rect1_MouseEnter(object sender, MouseEventArgs e) { Rect1MouseMove.Begin(); } private void Rect1_MouseLeave(object sender, MouseEventArgs e) { Rect1MouseMove.Begin(); } private void Ellipse1_MouseEnter(object sender, MouseEventArgs e) { EllipseMouseEnter.Begin(); } private void Ellipse1_MouseLeave(object sender, MouseEventArgs e) { EllipseMouseLeave.Begin(); } private void Path_MouseLeftButtonDown(object sender, MouseButtonEventArgs e) { PathClick.Begin(); } } }

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This mapping of source to transformation to destination is called a graph. A graph is a set of connected Data Flow components. One graph represents, for example, the data population of one table from another table. Unlike DTS 2000 s Transform task, a single Data Flow task can also update multiple tables (which is to say, multiple graphs).

Pie charts are usually the starting point for a visual designer or a BI professional. It is usually a great starting point because, as mentioned before, pie charts are readily understood by almost all users. Furthermore, a pie chart delivers insight on part-to-whole analysis very effectively. Pie charts are effective visualizations when these conditions are met: There are no more than six to ten parts to visualize (depends on the size of the pie slices). Pie slices are large enough to be noticeable when rendered in a pie chart. Colors can be used effectively to distinguish slices.

You need to animate UI objects using techniques to control how an animation interpolates over time, so that you can achieve more realistic effects like acceleration and deceleration.

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As SQL Server is used more and more for the largest enterprise data warehousing projects in the world, it becomes increasingly important for the core product to provide the majority of dataquality functionality required for these implementations The new Data Profiling task helps to deliver this functionality It provides support for eight different profiles: five that profile at the column level and three that provide profiling between columns The Data Profiling task provides the following data profiles for individual columns: Column Length Distribution: Provides the distinct lengths for string values with the table, along with the percentage distribution for each length This profile can help identify datalength problems that are common when migrating data from legacy systems that may not have enforced column lengths properly Alternatively, this profile could be used to determine the required column lengths for a new table that will hold data from many different sources.

Pie chart visualizations are an excellent choice when the data fits the mold of visualization. However, in data-driven applications, this is not always the case. Figure 6-5 illustrates two scenarios where pie chart visualizations make very poor implementation choices of part-to-whole analysis. On the left-hand side, we see a pie chart that includes pie slices that are barely legible. On the right-hand side, we see a pie chart that includes so many slices that it makes finding the appropriate slice within the legend very difficult.

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