Description:

The Rename Column step changes the names of one or more columns in an ETL dataset without modifying the underlying data values.

This transformation enables ETL designers to configure mappings that rename source columns to new names required by downstream applications, databases, reporting tools, APIs, or data warehouses. It is commonly used when integrating data from multiple systems that use different naming conventions or when preparing datasets for standardized enterprise reporting.

Column renaming rules are configured through the Configure Columns property, where one or more source-to-destination column mappings can be defined.

The Rename Column step is typically used after importing data and before performing additional transformations, validations, aggregations, or exporting the processed dataset.

The step supports:

• Renaming one or more columns
• Multiple column mappings
• Schema standardization
• Data integration
• Output schema customization
• Enterprise ETL workflows
• Integration with FlowWright ETL pipelines

This step can be used for:

• Standardizing column names
• Preparing datasets for external systems
• Database migrations
• Data warehouse loading
• API integrations
• Business reporting
• Enterprise ETL automation

Usage:

The Rename Column step is typically placed after importing data and before downstream transformations that depend on standardized column names.

During execution, the transformation applies each configured rename mapping to the dataset. Only the column names are changed; the underlying record values remain unchanged. Downstream ETL components reference the renamed columns.

A typical ETL pipeline might look like this:

Typical rename scenarios include:

• Mapping source system field names to enterprise naming standards
• Preparing datasets for database imports
• Aligning API response fields with destination schemas
• Simplifying complex column names
• Converting legacy field names to standardized names
• Preparing datasets for reporting and analytics
• Normalizing schemas from multiple source systems

After the columns have been renamed, downstream ETL components can reference the updated schema for validation, transformation, aggregation, or export.

Prerequisite:

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

Example:

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

• Create a new ETL definition named “clsRenameColumnTransformDef” and open it in designer mode. 
• Drag “clsInputCSV, clsRenameColumnTransform, and clsOutputCSV” steps to the canvas. 
• Connect the dots between “clsInputCSV” and the 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 "clsRenameColumnTransform" 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 "clsRenameColumnTransform" 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 the column from the drop-down list to rename. 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 transformation renames each configured column according to the defined mappings. The transformed dataset is then passed to downstream ETL components for additional processing, reporting, or export.

Tips:

• Rename columns early in the ETL pipeline so subsequent transformations reference consistent field names.
• Use descriptive and standardized naming conventions across all ETL definitions.
• Ensure renamed column names are unique within the schema.
• Document column mappings when integrating multiple source systems.
• Verify downstream transformations after renaming columns to ensure they reference the updated schema.
• Use meaningful business names instead of system-specific abbreviations whenever practical.
• Combine this transform with JSON Input File, Set Field Value, Validate Data, Sorting Transform, Aggregate Transform, Transform Using Regular Expression, or JSON Output File components to build complete ETL workflows.

Notes:

• This transformation changes only column names and does not modify the underlying data values.
• Multiple column mappings can be configured within a single transformation.
• Source columns referenced in the mappings must exist in the ETL schema.
• Downstream ETL components should reference the renamed columns rather than the original names.
• Duplicate destination column names should be avoided to prevent schema conflicts.
• Invalid column mappings or schema inconsistencies may result in ETL execution errors.
• This transform is commonly used during data migration, system integration, reporting, business intelligence, and enterprise data standardization projects.
• Validate the resulting schema before deploying ETL definitions to production environments.

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:

• Source column mappings
• Destination column names
• ETL schema validation
• Downstream transformation configuration
• Destination mappings
• Environment-specific settings

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

Click here to download the sample file.