Mapping Data in the Mapping Canvas

Before You Get Started

Before you begin mapping data, make sure you have completed the following actions:

1. Review the Navigating the Mappings Page in Reactor article to learn about all the features of the Mappings page and the Using the Semantic Layer in Reactor to learn how to use the Semantic Layer in Reactor's Mappings interface to transform source data to destination schemas
2. Connect all data sources needed for your mapping exercise (for more details, see: Connect to a Data Source)

3. Standardize data for your connected data sources (for more details, see: Standardize Your Source Data)

4. (Optional) If multiple data sources need to be linked so that you can join data between the sources, configure source links (for more details, see: Source Links)

Mapping Data

How to Create a Model Configuration

To create a new mapping configuration:

1. Select the ➕ icon at the top of the mapping graph next to the Models header to begin the setup wizard

2. Define the model's settings in the first screen of the setup wizard:

3. Model Purpose: Select the type of model configuration to create, one of the following:
   • Source Mapping if you want to map data from a data source (one of the white boxes on the mapping graph)
   • Select Model Mappingif you want to map data from a mapping configuration (one of the white boxes on the mapping graph)
4. Display Name: Enter an alias, or display name, for your mapping configuration.
   • Select Next to proceed. This alias will be displayed in the model configuration box on the mapping graph.
5. Description: Provide a summary of the model that other users can reference.
   • Select Next to move to the next screen of the setup wizard and define the model's input settings:
     
6. Select the object or model that inputs to the model you are creating: 
   • If you select Source in the Model Purpose option on the previous screen, select the source you wish to map from the Source dropdown
   • If you select Model in the Model Purpose option on the previous screen, select the model configuration from which you wish to branch in the Model dropdown.

     ℹ️ If you want to connect additional models, you can do so after the model is created directly in the Mapping graph. See "Managing Inputs in the Mapping Canvas" to learn more.

     ℹ️ Propagating inputs is helpful when an entire source or model object needs to be included in a model's output. This saves time by preventing the need to "remap" data when creating a mapping tree that includes two or more layers of models. See Using the Semantic Layer in Reactor for more information about transforming data using a multiple-model mapping tree.

   • (Optional) If you want all data from the input (Source or Model) selected above to output in this model, select the checkbox next to Propagate Inputs. 
7. Select the type of model to load into the Mapping graph:
   • Select Custom Model (the lower right option) to build an entire model from scratch.
   • If a default model configuration exists for the input you have selected, select the Load default Mappings and Models option to load and edit that configuration in the Mapping UI.

     
     
     

   ℹ️ The capability to load starter models for entities like orders, customers, products, and more is coming soon, keep an eye on our Release Notes page to be the first to know when this feature is released.

8. Select Next to move to the next screen of the setup wizard:
   • (Optional) Select the checkbox next to Coerce empty strings ('') to null if you want any mappings that result in an empty string to be converted to a null in the final output.
   • (Optional) Select the checkbox next to Remove null fields from Results preview and downstream outputs if you want fields resulting in a null value to be hidden from any results previews and outputs.
9. Click Finish to complete the wizard.
   • Once you select Finish, you will be directed to the mapping configuration screen for this configuration, where you can create, update, and delete mappings from the configuration.
   • For more info on writing mappings in a configuration, see "Writing Mapping Expressions". To learn more about
   • Select the Deploy button to deploy your changes if you selected the Load default Mappings and Models option while defining the model's Input settings and want to immediately deploy the default model. See the "Review and Deploy Your Changes" section below to learn more about the deployment step.
   • Read on below to learn how to add fields to a mapping configuration.

How to Add and Configure Fields in a Mapping Configuration

In most cases, adding and configuring a mapping takes seconds: 

To add a field to a mapping configuration, select the Add Field ➕ button at the top-left of the Model Mappings window, then select a field type from the field type dropdown menu. In mapping configurations, users can configure four types of fields:

• Single Value

• Local Variable

• Object Field

• Array of Objects

Read on to learn how more about each field type, including how to configure each field type.

Single Value Fields

A Single Value field is an attribute with one output per mapped record. It is the most common type of field used in mapping, defines a single attribute or field in a mapping output.

To configure a single value field, select the 📝 icon next to the field, then enter the following:

• In the Field Name box, enter the name of the field in the output. This would be the name of a field in a semantic model or a database table, like sku, source_order_id, first_name, etc.
• In the Description box, enter a description of the field that tells other users in your organization what data is stored in it.
• In the Mapping Expression box below the description, write a mapping expression for the field you have created (for more information on writing mapping expressions, consult this article: Writing Mapping Expressions).

When you are finished, select the Done button to return to the Mapping Editor main screen.

Local Variables

A Local Variable is a shortcut that a user can configure, consisting of two parts:

• The variable name

• The expression that the variable name represents

Local variables are useful when mapping sources with deeply nested fields. For instance, in Shopify orders, refund line item data is stored in a deeply-nested object: refunds.refund_line_items.line_item. A field in this object (like the sku field) can be referenced in a mapping expression like this: source.refunds.refund_line_items.line_item.sku. Alternatively, we can create a local variable for source.refunds.refund_line_items.line_item (which we will call rli), so that the sku field can be referenced like this instead: rli.sku.

To configure a Local Variable, enter the following information:

• In the Field Name text box, enter the name of the local variable to use in mapping expressions. In the example above, "address" is the field name.

• In the Description text box, enter a brief description of the variable so that other users in your organization will understand what it is and why it is used. In the example above, "A variable (alias) for source.customer.default_address", would suffice.

• In the Local Expression Editor box below the description, write an expression that the local variable represents. In the example above, this would be source.customer.default_address

Object Fields