How to Check a CSV Before Importing It

Before importing a CSV, check four things: the file structure is valid, the required column names match the destination, important values use the expected formats, and the destination accepts the file in a preview or small test import. A CSV can be perfectly valid and still fail if the receiving system expects different headers, dates, numbers, IDs, or allowed values.

For a detailed explanation of CSV structure problems and how to repair them, use the CSV validation guide.

Use this checklist before an important import

It is worth checking a CSV when:

• the file came from another system or team,
• you changed it in Excel or another spreadsheet app,
• the import contains many rows and would be painful to undo,
• the destination has strict required columns or formats,
• you are replacing or updating existing records.

The goal is simple: catch easy problems before the destination does.

Four things to check before importing

1. Check the CSV structure

Run the validator first. Look for rows with the wrong number of columns, broken quotes, empty or repeated headers, damaged characters, separator problems, and other format issues.

Fix serious problems before you move on. If you need help with a specific finding, see the CSV validation guide.

2. Check the column names

Compare the CSV header with the destination's import instructions.

Make sure required columns exist and use the names the destination expects. customer_id and customerId may look similar to you but still be different to an importer.

Also check that no important column was accidentally renamed, deleted, or duplicated during spreadsheet editing.

3. Check the values and formats

Look at the columns that matter most to the destination:

• dates should use the format the destination expects,
• numbers should use the expected decimal style,
• IDs should keep leading zeros when they matter,
• required cells should not be empty,
• values such as status or country should use accepted choices.

Spreadsheet apps can silently change dates, long numbers, and IDs. If you are unsure what is actually in each column, use the CSV profiler.

4. Test the import safely

If the destination offers a preview, dry run, test workspace, or small sample import, use it before the full file.

A general CSV check cannot know every rule in another system. The destination's own preview is the final place to catch mapping or business-rule problems.

Step-by-step pre-import check

1. 01

   Export the final CSV

   Use the exact file you plan to import, not an earlier copy.

2. 02

   Run the CSV validator

   Choose the final CSV and fix serious structure problems first. Check the validator page for its current file-size limit.

3. 03

   Compare the headers

   Check every required destination column and make sure the names match.

4. 04

   Check important value formats

   Review dates, numbers, IDs, required fields, and any allowed-value lists.

5. 05

   Use a preview or small test if available

   Confirm the destination maps the columns and values the way you expect.

6. 06

   Import the full file

   Only proceed when both the CSV check and the destination check look right.

How to use the validator result

Treat serious structure problems as a stop sign. Fix them before the import.

Warnings deserve attention when the destination is strict about separators, text encoding, or line endings. Smaller cleanup items such as extra spaces or empty rows may also matter when IDs must match exactly or row counts must stay the same.

If the validator reaches its current findings limit, fix the problems it shows and run it again before you continue with the destination checks. The validator page lists the current limit.

What the validator cannot know

The validator cannot know the rules of your CRM, shop, accounting package, database, or other destination.

For example, a CSV can be structurally fine but still fail because:

• a required column is missing,
• a date uses 03/04/2026 when the destination expects 2026-04-03,
• an ID such as 00125 was changed to 125,
• a decimal comma is used where the destination expects a decimal point,
• a status value is not one of the allowed choices.

That is why this page stays separate from the validation guide: validation checks the file itself; this checklist checks whether the file is ready for a particular destination.

What happens to your file?

The validator processes the CSV on your device. The file is not uploaded to our servers.

The check does not change your original CSV.

If you use a third-party destination preview or import after this check, that destination has its own privacy and upload rules. Review those separately when the file is sensitive.

Practical limits

The validator accepts one CSV up to 200 MB. The cap follows the same registry method: the displayed limit comes from the shipped tool registry and is backed by first-party browser measurement. The first-party measurement evidence is recorded in docs/tool-audit/batch-a.md. Your destination may have different file-size, row-count, or format limits, so check its import instructions too.

Simple example

You have a customer CSV with these columns:

customer_id,email,signup_date
00125,ana@example.com,03/04/2026

The CSV structure can be completely valid. But the destination may require signup_date as YYYY-MM-DD, and a spreadsheet may have removed the leading zeros from some IDs.

The pre-import checklist catches both kinds of risk: first the CSV structure, then the destination-specific values.

Result: you import a file that is both valid CSV and suitable for the system receiving it.

If the destination fields do not match the source, use the cross-system field-mapping guide. If the fields are right but the importer requires different header names or order, use the rename and reorder CSV columns guide.

U.S. import example: valid CSV, different destination rule

customer_id,state,amount,signup_date
00125,CA,1234.50,12/31/2026

This is structurally ordinary comma-delimited CSV with a decimal point and a U.S.-style date. A destination can still require signup_date as 2026-12-31. That conversion is a destination-format check, while preserving 00125 is a data-integrity check.

Check a CSV before importing

See the transformation

Structural problems become a precise review queue

See the exact rows with formatting problems while leaving the original file unchanged.

Input

ordercustomertotal

1041ana@…149.90

1042marc@…

1043“lina@…86.50

Result

Row 42· ragged row

Row 87· unclosed quote