Description:

The Set Field Value step updates one or more fields in an ETL dataset by assigning configured values during ETL execution.

This transformation allows designers to configure field mappings that populate existing columns with constant, default, replacement, or calculated values, based on the configured transformation rules. It is commonly used to standardize imported datasets, fill missing values, assign default statuses, initialize fields, or prepare records for downstream processing.

The transformation rules are configured via the Configure Field property, which allows one or more field mappings to be defined.

The Set Field Value step is typically used after importing data but before validation, aggregation, reporting, or loading records into destination systems.

The step supports:

• Setting constant field values
• Assigning default values
• Updating existing fields
• Multiple field mappings
• Data normalization
• Dataset enrichment
• Integration with FlowWright ETL pipelines

This step can be used for:

• Populating default values
• Standardizing imported records
• Assigning processing status
• Initializing workflow fields
• Preparing migration datasets
• Business rule implementation
• Enterprise ETL automation

Usage:

The Set Field Value step is typically placed after data import and before validation, aggregation, or export of the processed dataset.

During execution, the step evaluates the configured field mappings and updates the specified columns with the configured values. The modified dataset is then passed to downstream ETL components for additional processing.

A typical ETL pipeline might look like this:

Typical field assignment scenarios include:

• Setting a default status value
• Populating missing department names
• Assigning import source identifiers
• Initializing workflow flags
• Updating processing indicators
• Standardizing category values
• Preparing records for reporting

The updated records continue through the ETL pipeline where they can be validated, transformed, aggregated, or exported.

Prerequisite:

The ETL process engine service should be running to execute the ETL definitions.

Example:

Let’s build and execute the “clsSetFieldValueTransformDef” example.          

• Create a new ETL definition named “clsSetFieldValueTransformDef” and open it in designer mode. 
• Drag “clsInputCSV, clsSetFieldValueTransform, and clsOutputCSV” steps to the canvas. 
• Connect the dots between the “clsInputCSV” and other steps, as shown above.
• Define a variable or a global required for the execution.
• Click the "clsInputCSV" step to configure its "Required" properties. Provide a name for the step. Specify the path to the input CSV file on the application server. Select the ETL data schema from the drop-down list. Note: The schema's column names should match those in the input file. Click the Save button. Click here to learn about the ETL Data Schema Designer.

• Click the "clsInputCSV" step to view its "Optional" properties. Select the Show Schema button. The pop-up window displays the schema columns as shown below. This function helps to understand the schema at a glance.  

• Click the "clsSetFieldValueTransform" step to configure its "Required" properties. Provide a name for the step. Click the button to configure the columns to transform. Then click the Save button. 

• Click the "clsSetFieldValueTransform" step to configure its "Required" properties. Click the column button to open the transform list. A pop-up window will appear for configuration. Click the Add Row (+) button to insert an empty row. Select a column from the drop-down list to set a value. Enter a new column name. You may also type a new column name that does not exist in the schema and press Enter to add it. Click the Save button. You may add multiple columns for transformation by using the Add Row button. 

• Click the "clsOutputCSV" step to configure its "Required" properties. Enter a name for the step. Enter the path to the output CSV file on the application server. Click the Save button. 

• Click the "clsOutputCSV" step to configure its "Optional" properties. Provide a name for the step and specify the variable or global reference to hold the virtual output path. Click the Save button. 

• The “Logging” configuration is necessary for documentation and also measures workflow progress and percent complete. This is achieved by configuring the step state and percent fields individually, as shown below. Configure the “Logging” using the following properties.

• Save the ETL Definition. Execute the ETL process. During execution, the configured values are assigned to the specified fields for each processed record. The updated dataset is then passed to downstream ETL components for validation, sorting, aggregation, reporting, or export.

Tips:

• Use this step to populate mandatory fields before performing validation.
• Configure default values for fields that may be empty in the source dataset.
• Keep field assignment rules consistent across ETL definitions to improve maintainability.
• Group related field assignments within a single transformation whenever practical.
• Use descriptive schema and column names to simplify configuration.
• Validate the resulting dataset after field assignments have been applied.
• Combine this transform with JSON Input File, Derived Column, Validate Data, Sorting Transform, Aggregate Transform, Transform Using Regular Expression, or JSON Output File components to build comprehensive ETL workflows.

Notes:

• Multiple field assignments can be configured within a single transformation.
• The configured fields must exist within the ETL schema.
• Assigned values overwrite the existing values for the configured fields according to the transformation configuration.
• Invalid field mappings or schema inconsistencies may result in ETL execution errors.
• This transformation modifies the processed dataset but does not alter the original source data.
• Field assignments are performed for each processed record during ETL execution.
• Verify the updated dataset before loading data into production systems.
• This transform is commonly used in data migration, customer master management, reporting, application integration, business intelligence, and enterprise data preparation workflows.

Definition Sample:

You may download the sample ETL definition(s) from the link provided and later import them into your FlowWright ETL Designer.

Note: Verify and complete any missing configuration after importing the sample, including:

• Field assignment mappings
• Source column mappings
• Assigned values
• ETL schema configuration
• Downstream transformation connections
• Environment-specific configuration

After verifying the configuration, save the ETL Definition before execution.

Click here to download the sample file.