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SmartPlant Foundation Help

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English
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SmartPlant Foundation
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SmartPlant Foundation / SDx Version
10
SmartPlant Markup Plus Version
10.0 (2019)
Smart Review Version
2020 (15.0)

Data Validator imports files in CSV format into the staging system where the validation takes place. Data Validator uses import definitions to map the column headers in the input CSV file to the correct column headers for objects, properties, and relationships in the staging area. The import mapping not only uses the physical column headers that already exist in the input data file, but it also supports other column headers that are computed or provided by the user during the import process.

We recommend using a text editor like Notepad to modify your CSV files to avoid any unexpected results.

Import definition consists of all the column headers that have defined in it. You can define the column headers for each import definition to map the column data from the imported CSV files to the existing columns in the target system. The mapping can be driven by the target system database and defined manually using the free text option, or you can use existing mapping in the local staging area. You can create, edit, and modify each column header mapping to match the class definitions, objects, properties, relationships, and relationship properties between the imported data objects and target system objects.

Column headers

Column headers are used to map the data in the CSV file to the correct locations in the staging area and target systems, and supports a number of different column types. The column type you select when defining the column header determines the appearance of the Column Header and Mapping dialog boxes that you use later in the import mapping process. Data Validator supports the following types of columns:

  • Physical - Maps to a specific column in the input data source.

  • Computed - Generates a new column using data from other columns or sources, such as environment type variables. This provides the ability to use functions. For example, you can enter a function in the Computed API box to replace original entries with new entries.

  • Constant - Allows you to use a string constant.

  • Prompted - Prompts the user for a value when a job is run, based on the job definition. The provided value is stored with the job definition for later use, if necessary.

  • Prompted API - Generates a list of prompts generated from other columns or sources, such as environment type variables, and displays this list using functions.

  • Prompted Picklist - Allows the user to pick an entry from a defined enumerated list in the local staging area when running a job.

In the following graphic, you’ll see that column headers mapped from the input CSV file to the staging objects. Additional column mappings allow users to enter values and the application to compute values during the import process.

Explanation of the mapping in the graphic:

  • Column 1 of input CSV file is mapped to the object called FDWTag and the Name property of it.

  • Column 2 of input CSV file is mapped to the Description property of the tag object.

  • Column 3 of input CSV file is not mapped directly, but is used in a computed column which is used to map to the object called FDWModel and the Name property of it.

  • The constant value “EQUIPMENT” is mapped to the Tag Category property of the tag object.

  • A prompted value allows the user to enter a contact name that is mapped to the Contact Name property of the tag object.

  • A prompted column allows the user to select a company name from the list, which mapped to the Company Name property of the tag object.

  • A prompted API allows the user to select a unit from the list of units available in the staging system. This is mapped to the PotentialUnit property of the tag object.

  • A computed column takes the input value from the Column 3 of the input CSV file and changes it to upper case. This is mapped to the object called FDWModel and the Name property of it.

You must create column mappings for each column header in the import definition so that imported data matches the corresponding data column format in the staging area or target system. You can search for the objects and properties found locally or add new objects and properties, then create the new mappings to match the target system objects and properties.

There are four types of column header mappings you can create for each column header.

  • Object mapping

  • Property mapping

  • Relationship mapping

  • Relationship property mapping