Advanced ETL Processor transforms text files by reading the source rows, mapping fields in the Transformer, changing values where needed, and sending the result to a Writer. Use this pattern when CSV, tab-delimited, or fixed-width files need cleaning before another system sees them. Text files are simple until someone puts commas inside names. Then they become educational.
What You Will Learn
- Read text-file data before transformation.
- Map input fields to output fields in the Transformer.
- Apply functions to clean strings, numbers, dates, and other values.
- Preview transformed rows before writing the final output.
Before You Start
Use a small sample file with awkward rows. Empty fields, quotes, strange dates, and extra delimiters are better found in a test run than in a production import log at midnight.
Text File Transformation Docs
The matching transformation reference is Transformer. It explains how Advanced ETL Processor changes data format, structure, and values between the Reader and Writer.
Use the Transformer Toolbar for mapping, record navigation, source-data preview, and processing controls. If no rows appear, check the previous step execution log before changing mapping rules.
The examples show common transformations such as splitting a date into day, month, and year, or making names more readable. Use Auto Mapping when source and output fields already line up.
For expressions, start with Transformation Functions, then use the dedicated docs for strings, numbers, dates, lookups, and regular expressions.
Need Advanced ETL Processor First?
Download the Advanced ETL Processor trial before following the tutorial, or review Enterprise if you need scheduled unattended execution.