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Previewing a Dataset

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Modified on Wed, 27 Feb 2013 04:43 PM Categorized as Datasets, Filtering
Click to return to: Documentation | Defining Datasets


Overview

This article shows how to use the Preview tab in the Dataset Designer to view the data generated through your dataset. You can use this preview option to quickly verify the definition of your dataset.

The top section of the Preview tab shows the list of filters defined by your dataset. You can optionally reconfigure the settings for a filter and then view its effect on the resulting data.

The Preview tab of the Dataset Designer.

The Preview tab of the Dataset Designer.


Previewing data from your dataset

To preview the data from your dataset:

  1. Select the Preview tab in the Dataset Designer.
  2. (Optional) Enter the maximum number of data rows you want to preview in the Maximum records box.
  3. Click Preview Data. The data records generated from your dataset are displayed in a grid and the number of records returned is shown above the grid.

Changing filter settings

Dataset query filters

The list of filters defined by your dataset is displayed in the Dataset Query Filters grid and includes both public and private filters.

To change the settings for a filter:

  1. Locate the filter in the Dataset Query Filters grid.
  2. Click the corresponding button in the Action grid column to reconfigure the filter. Depending on the filter type, proceed as follows:
    • For a filter on a dataset column, the Data Filter Setup Wizard is displayed. Follow the steps as shown in the article, Defining Dataset Columns, to reconfigure the filter.
    • For a filter on an existing standard dimension, the Standard Dimension Setup Wizard is displayed. Follow the steps as shown in the article, Using an Existing Dimension, to reconfigure the filter.
    • For a filter on an existing time dimension, the Time Dimension Configuration Wizard is displayed. Follow the steps as shown in the article, Using an Existing Dimension, to reconfigure the filter.
  3. Click Preview Data to view the resulting data from your dataset.

External filters

If the virtual table that your dataset is based upon defines its own public filter, that filter will be listed in the External Child Structure Filters grid in the Preview tab. External filters are essentially inherited filters which you can further configure from the Preview tab. For example, you can make an external filter public, so that it can be accessed at the dashboard design stage. In Dundas Dashboard v4 or later, you have the additional option of replacing an external filter with an existing full dimension.

The External Child Structure Filters grid.

The External Child Structure Filters grid.


To reconfigure an external filter:

  1. Locate the filter in the External Child Structure Filters grid.
  2. Click the Customize button in the Action grid column. The External Data Filter Setup Wizard is displayed. See Defining a Filter for details on configuring filter settings.

Non-empty filtering (OLAP)

For OLAP-based datasets, the Non-Empty Filtering option lets you obtain dataset results in which the selected measures and dimensions have non-empty values.

Non-Empty Filtering button for OLAP-based dataset.

Non-Empty Filtering button for OLAP-based dataset.


The list of available measures is based on the underlying virtual cube. The list of available dimensions is based on the dimensions chosen for your dataset.

Select measures and dimensions for non-empty filtering.

Select measures and dimensions for non-empty filtering.


Viewing the generated SQL/MDX query

Follow the steps below to view the generated SQL (or MDX) query that corresponds to this dataset. You can use this to help troubleshoot performance or other issues.

  1. Edit the dataset.
  2. Type ddquery. The Query dialog appears and displays the text of the generated query. You can copy the text to your clipboard or close the dialog when finished.

Note that there are some limitations to this option:

  • If temporary tables are required, the query cannot be shown (⪚ if non-permanent time dimensions and/or stored procedures are used).
  • For relational datasets (⪚ SQL), parameter values will not be shown. Parameters will be substituted with placeholders like: @p01, @p1, or ? ? ?

Related topics


Click to return to: Documentation | Defining Datasets

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