Creating an Analysis and Model data mart

You can create a custom data mart based on an Analysis or Model using the Data Marts wizard.

Before you begin

Access the Data marts page. For more details, see Managing-data-marts-for-custom-views-reports-and-report-templates.

To add a data mart for an Analysis or Model

1. On the Data marts list page, select Add > Data mart based on Analysis/Model execution. By default, the General tab is displayed. Tab or properties marked with 🔵️ are available in advanced mode only.
2. In the General tab, set the following properties:
   • Name: Required name for the data mart.
   • Identifier: Indicates the unique identifier for the data mart. Every identifier is prefixed with 'ER_V_'. By default, the identifier is suggested based on the data mart name. You can edit this value. The identifier must contain only letters, numbers or underscores, and it cannot be empty. This identifier can be used in the SQL query while creating SQL-based dependent data marts.
     
     If you modify the identifier of the data mart that is currently in use by one or more data marts, the dependent data marts will stop working. A warning message is displayed with a list of dependent data marts. You can choose to revert the changes or proceed with the update. If you choose to proceed, you must update the identifier in the dependent data marts to continue to use them.
   • Description: Brief description for the data mart.
   • Select an analysis or a scenario: Select an analysis or scenario from the list populated.
     
   • Materializer task: Select the task for materializing the Analysis/Model View. For details, see Configuring-the-ER-View-Materializer-task.
   • Tablespace: Name of the tablespace where the generated table will be materialized. If this field is blank, BMC TrueSight Capacity Optimization's default tablespace is used.
   • Materialize validity: Set the validity of the Analysis/Model View, for example, 1 day. Setting the validity to zero will cause the Analysis/Model View to be materialized every time it is requested. By default, 12 hour validity is applied. 
   • Do not apply limits on row and series: Use this option to set a limit in the data mart. By default this option is set to No. An analysis/model might only display a maximum number of series and a maximum number of charts (which can be customized). This option can be used to remove the default limits present in an analysis/model.

   • Entity filters: Allow you to select one of the following options to specify if the entity filters must be changed or not:

     • Leave existing entity filters: Select this option to retain the existing entity filters.
     • Remove domain related entity filters: Select this option to remove the container domain related entity filters.

     

3. (Only for the Analysis data marts) In the Column mapping tab, set a name for each column that will be displayed in the Analysis View output. The list of available columns is dynamically generated at the creation time and depends on the selected analysis. The mapping can be refreshed at any time clicking , to reflect any changes that were made on the analysis. Specify the following for each column:

   • Analysis View mapping: Name of the analysis/model output column to be mapped to a DB table column
   • Column name: Enter the name of the output DB column. It can have a maximum of 30 characters, no spaces, and cannot begin with a number. If you leave the Column name field empty, the column will not appear in the Analysis View output.

   Note: This tab is not displayed when you select a model scenario in the General tab. 

4. 🔵️ In the Index tab, define indexes for the generated table.
5. 🔵️ In the Privileges tab, you can grant access rights to the Analysis/Model View, to specific database users or roles.
6. Click Save.

The Analysis/Model data mart is created.

Data mart columns

For the data marts created based on an Analysis, you can configure the columns to be displayed in the Column mapping tab. 

For the data marts created based on a Model, some columns are fixed and some are dynamically generated. Review the following table for columns displayed in the Model data mart: 

*For example, if the selected analysis or model includes the CPU_UTIL metric, the following columns are displayed along with the fixed columns: 

• CPU_UTIL_TR
• CPU_UTIL_SATV
• CPU_UTIL_AVG
• CPU_UTIL_PEAK
• CPU_UTIL_IS
• CPU_UTIL_VS
• CPU_UTIL_SAT
• CPU_UTIL_SATP