DataWorks:Logical model: Application table

Create an application table

1. Go to the Data Modeling page.

   Log on to the DataWorks console. In the top navigation bar, select the desired region. In the left-side navigation pane, choose Data Development and O&M > Data Modeling. On the page that appears, select the desired workspace from the drop-down list and click Go to Data Modeling.

2. On the Data Modeling page, click Dimensional Modeling in the top menu bar to go to the Dimensional Modeling page.

3. Create the Application Table.

   1. On the Dimensional Modeling page, hover over the icon and click Logical Model > Create Application Table.

   2. Configure the basic information for the Application Table.

      

      Parameter	Description
      Data Layer	The Data Layer where the Application Table resides. By default, the Application Data Service (ADS) Layer of the Application Layer is selected. You can also place the Application Table in other data layers based on your business requirements. For more information about how to create a Data Layer, see Define a data layer.
      Mart/Subject	Select a created Data Mart or Subject Area. For more information, see Data Mart and Subject Area.
      Granularity	Select a dimension that defines the table's Granularity. For more information, see Create a conceptual model: Dimension.
      Period	Specifies the time range for the statistical data to be consolidated in the Application Table, such as Last 1 Day or Last 7 Days. You must select from existing Periods. If no existing Period meets your business requirements, you can create one. For more information, see Period.
      Modifier	Specifies the business scope of the statistical data. You must select from existing Modifiers. If no existing Modifier meets your business requirements, you can create one. For more information, see Modifier.
      Naming Rule	A checker that enforces the table naming conventions. You can select a checker that you created for a Data Layer during data warehouse planning. For more information, see Configure a data layer checker and Use a checker.
      Name	The name of the Application Table. If a Naming Rule is configured, the table name must comply with the rule.
      Display Name	The display name of the table.
      Lifecycle	The retention period of the table in days. An Application Table can be retained for up to 36,000 days.
      Owner	The Owner of the Application Table. By default, this is the user who created the table.
      Description	The description of the table.

4. Click Save in the upper-left corner to save the Application Table configuration.

Add fields to the table

You can add Fields to the table in Shortcut Mode or Script Mode. Shortcut Mode supports the following import methods:

• Import from Table/View: Import Fields from an existing physical table or view in a compute engine. Search for and select an existing physical table or view from the Search for Existing Table/View drop-down list.

  Note

  Currently, you can only import Fields from tables or views in MaxCompute, Hologres, and E-MapReduce (EMR) Hive engines.

• Import from Metrics: Select Derived Metrics to add as Fields to the model.

Configure field information

After adding Fields, you can configure the Associated Field, Redundant Field, and Associated Granularity/Metric for each Field based on your business requirements.

1. Configure Field properties.

   By default, the Field list displays basic properties such as Field Name, Data Type, Field Display Name, Description, Primary Key, Not Null, Measurement Unit, and Actions. In the upper-right corner of the Field list, click Field Display Settings to select which properties to display and modify them as needed.

2. Set the Associated Field Standard for the Fields.

   Associating Fields with a Field Standard normalizes their value content and range.

   A Field Standard unifies data that shares the same meaning but has different Field names by defining common value ranges, units of measure, and other attributes.

3. Set the Redundant Field.

   In a traditional star schema for Dimensional Modeling, dimensions are stored in dimension tables and accessed through foreign keys in the fact table to reduce storage consumption. In Intelligent Data Modeling, you can designate frequently used Fields, such as user IDs or common analytical dimensions, as Redundant Fields. This practice improves downstream query efficiency, simplifies data access, and reduces table joins.

   In the Actions column for a Field, click Redundant Field to configure its associated Fields.

   

4. Set the Associated Granularity/Metric.

   For Aggregate Tables and Application Tables, for each Field, you can set an Association Type to specify the statistical type of its value. The available types are Statistical Granularity, Derived Metric, and Atomic Metric.

   • Statistical Granularity: Used to associate with dimension tables and Fields within those tables, such as product dimension or seller dimension.

   • Derived Metric: Specifies the Derived Metric for the statistical value that the Field represents. For example, the total payment amount for orders placed on the Hema App in the last 7 days.

   • Atomic Metric: Specifies the Atomic Metric for the statistical value that the Field represents. For example, the total payment amount.

   Note

   For Fields imported from a table or added in Script Mode, no default association type is set. You must set the association type manually.

   After configuration, you can specify the exact object to associate with the Field in the Field Association column of the Field list.

5. When you finish the settings, click Save in the upper-left corner.