How to map CSV columns to a destination schema
Column mapping connects the meaning of a source table with the fields a destination expects. It is useful before import when headings use different names or the destination requires a particular order.
Start with the right material.
- What you provide
- a visible source table and target headers supplied by the user.
- The intended result
- Reorder and rename fields to the target schema; flag unmapped fields.
How the workflow works.
Provide the source headers, sample values and target schema. Match fields by meaning, identify required transformations and keep unmapped or ambiguous columns visible.
What to check in the result.
Review the mapping with representative rows before using it broadly. Similar column names can describe different values, and missing required data cannot be solved by merely renaming a header.
Import the CSV with a consistent delimiter and inspect text identifiers, dates and amounts before using spreadsheet totals. Keep the original file available while checking any automatic type conversion.
Learn from the worked example.
The downloadable CSV example uses these columns: company, email. Compare a complete record with the source before sorting, combining or importing your own output.
The supplied example is illustrative. It is not a recorded run on your data.
Download the example ↓Use it with your own material.
- Open the source you want to work with and activate WebAct on that tab.
- Copy the task from the hero, add any required fields or preferences, and review the proposed actions.
- Compare the first result with your source. Ask for a specific correction or refinement where needed.