Description:

The Transform Text Case step changes the capitalization of text values in one or more ETL columns. The transformation is configured through the Select Case Type property, where ETL designers select the desired case conversion and the columns to which it should be applied.

Enterprise data frequently originates from multiple sources with inconsistent capitalization. Customer names, department names, addresses, product descriptions, countries, and business identifiers may use mixed case, all uppercase, or all lowercase values. This transformation provides a simple and consistent way to normalize text presentation without changing the underlying business meaning.

The Transform Text Case step is commonly used after text cleansing operations such as trimming and character removal but before validation, duplicate detection, reporting, exporting, or loading data into downstream systems.

Unlike Transform Text Replace, which substitutes specific text values, this transformation changes only the capitalization of existing text while preserving the original characters.

The step supports:

• Uppercase conversion
• Lowercase conversion
• Title case conversion
• Sentence case conversion
• Multiple column transformations
• Enterprise ETL workflows
• Integration with FlowWright ETL pipelines

Typical business uses include:

• Standardizing customer names
• Formatting addresses
• Normalizing department names
• Preparing reporting data
• Improving search consistency
• Standardizing exported datasets
• Enterprise data quality initiatives

Usage:

The Transform Text Case step is typically inserted after importing and cleansing text data but before validation, filtering, grouping, duplicate detection, reporting, or exporting.

During execution, each configured source column is processed using the selected case conversion. The transformed values immediately become available for downstream ETL components.

A typical ETL pipeline might look like this:

Typical usage scenarios include:

• Converting customer names to Title Case
• Standardizing country codes to UPPERCASE
• Converting email addresses to lowercase
• Formatting department names consistently
• Normalizing product descriptions
• Preparing searchable datasets
• Standardizing exported reports

The transformed values can then be validated, filtered, grouped, indexed, exported, or loaded into downstream business systems.

Prerequisite:

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

Example:

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

• Create a new ETL definition called “clsTransformCaseDef” and open the definition in designer mode. 
• Drag “clsInputCSV, clsTransformCase, 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 store 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 "clsTransformCase" step to configure its "Required" properties. Provide a name for the step. Click the button to configure the columns and operation type, then click the Save button. 

• Click the "clsTransformCase" step to configure its "Required" properties. Click the button to open the transform list configuration window. Click the Add Row (+) button to insert an empty row. Select the text column and the case transform function (upper, lower, title, and Pascal case) from the drop-down list. 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. Provide the variable or global reference to store 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 “clsTransformCase” step should transfer the text case as configured to the output file. The variable or global holds the output file’s virtual path as shown below. 

Tips:

• Trim text values before changing their case to remove unnecessary whitespace.
• Convert email addresses to lowercase to improve consistency and duplicate detection.
• Use Title Case for names and addresses to improve readability.
• Apply uppercase formatting when exporting standardized business codes.
• Verify downstream application requirements before selecting a case conversion.
• Test transformations using representative production datasets before deployment.
• Combine this transform with Transform Text Trim, Transform Remove Character, Transform Text Replace, Validate Data, Remove Duplicates, JSON Output File, or SQL Output components to create comprehensive ETL data standardization workflows.

Notes:

• Multiple case transformation rules can be configured within a single ETL step.
• Source columns must exist within the ETL schema.
• Only the capitalization of text is modified; the underlying characters remain unchanged.
• Successfully transformed values immediately become available to downstream ETL components.
• Invalid column mappings or transformation configurations may result in ETL execution errors.
• This transform is commonly used for customer information, addresses, employee records, product catalogs, ERP migration, CRM integration, regulatory reporting, and enterprise data quality initiatives.
• Review downstream reporting and search logic whenever capitalization standards are modified.

Transform Text Case vs. Transform Text Replace:

Although both transformations modify text values, they are intended for different purposes:

As a general guideline:

• Use Transform Text Case when only the capitalization of text needs to be standardized.
• Use Transform Text Replace when text values themselves must be changed or corrected.

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:

• Case transformation rules
• Source column mappings
• Case type selection
• ETL schema validation
• Downstream transformation mappings
• Destination component configuration
• Environment-specific settings

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

Click here to download the sample file.