Description:

The Transform Combines Columns Data step combines values from multiple source columns into a single destination column. The transformation is configured using three properties:

• Select Output Column – Specifies the destination column that will store the combined value.
• Input Delimiter – Specifies the delimiter inserted between each source value.
• Configure Transform List – Defines the source columns that will be combined into the output column.

Many enterprise ETL processes receive data in separate columns that must later be merged into a single formatted value. Examples include combining first and last names, constructing mailing addresses, generating display names, creating composite identifiers, or preparing export files for external systems.

The transformation preserves the original source columns while generating a consolidated output column that can be used throughout the remainder of the ETL pipeline.

Unlike Transform Split Column Data, which separates one column into multiple values, this transformation performs the opposite operation by merging multiple columns into one.

The Transform Combines Columns Data step is typically placed after data cleansing and validation but before exporting, reporting, or loading into downstream applications.

The step supports:

• Combining multiple columns
• Configurable output column
• Configurable delimiters
• Composite value generation
• Enterprise ETL workflows
• Integration with FlowWright ETL pipelines

Typical business uses include:

• Creating full names
• Building complete mailing addresses
• Constructing display values
• Generating composite business keys
• Producing export-ready text
• Preparing legacy system imports
• Standardizing reporting datasets

Usage:

The Transform Combines Columns Data step is typically inserted after cleansing and validating imported data but before exporting, reporting, database loading, or downstream integrations.

During execution, each configured source column is read in the configured order. The values are concatenated using the specified delimiter, and the resulting value is written to the selected output column.

A typical ETL pipeline might look like this:

Typical usage scenarios include:

• Combining FirstName and LastName into FullName
• Building a complete postal address
• Creating searchable display values
• Constructing product descriptions
• Combining city, state, and postal code
• Generating composite business identifiers
• Preparing export files for external ERP or CRM systems

The combined output can then be validated, exported, grouped, indexed, or loaded into downstream business applications.

Prerequisite:

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

Example:

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

• Create a new ETL definition called “clsTransformCombineColumnsDef” and open the definition in designer mode. 
• Drag “clsInputCSV, clsTransformCombineColumns, and clsOutputCSV” steps to the canvas. 
• Connect the dots between the “clsInputCSV” step and other steps, as shown above.
• Define a variable or a global variable to hold the virtual path. 
• 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 "clsTransformCombineColumns" step to configure its "Required" properties. Provide a name for the step. Select the output column from the drop-down list. Enter the input delimiter. Click the button to configure the combined columns. Click the Save button. 

• Click the "clsTransformCombineColumns" step to configure its "Required" properties. Click the 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 the text column to combine from the drop-down list. Click the Save button. Click the Add Row button to insert multiple columns for transformation. 

• 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, create a new ETL instance, and execute. Render the ETL instance. Click the ETL step to view its properties. The “clsTransformCombineColumns” step should combine the text columns as configured into the output file. The variable or global holds the output file’s virtual path as shown below. 

Tips:

• Trim text values before combining columns to eliminate unnecessary whitespace.
• Select a delimiter that matches the formatting requirements of downstream systems.
• Ensure the output column exists within the ETL schema before configuring the transformation.
• Keep the original source columns if they may be required for auditing or later processing.
• Verify delimiter selection when generating composite identifiers to avoid ambiguity.
• Test the combined output using representative production data before deployment.
• Combine this transform with Transform Text Trim, Transform Split Column Data, Validate Data, Rename Column, Output Column Transform, JSON Output File, or SQL Output components to build comprehensive ETL data preparation workflows.

Notes:

• The destination column must exist within the ETL schema before execution.
• Source columns are combined in the configured order.
• The configured delimiter is inserted between each source value.
• Multiple source columns can be combined into a single destination column.
• Original source columns remain available unless explicitly removed later in the ETL pipeline.
• Invalid column mappings or configuration errors may result in ETL execution errors.
• This transform is commonly used for customer information, address formatting, product descriptions, ERP migration, CRM integration, reporting, regulatory exports, and enterprise data normalization.
• Review downstream mappings whenever the output column structure or delimiter configuration changes.

Transform Combines Columns Data vs. Transform Split Column Data:

Although both transformations work with multiple columns, they perform opposite operations:

As a general guideline:

• Use Transform Combines Columns Data when multiple fields must be merged into a single formatted value.
• Use Transform Split Column Data when a single composite value must be separated into individual columns.

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:

• Output column selection
• Delimiter configuration
• Source column mappings
• Transformation order
• ETL schema validation
• Destination component configuration
• Downstream transformation mappings
• Environment-specific settings

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

Click here to download the sample file.