How to Validate CSV Data Before It Reaches Production

Spot-checking a few rows tells you those rows look fine, it tells you nothing about row 8,472. Validation is the difference between "I looked at some of it" and "I know every row satisfies these rules."

Three Kinds of Validation Rules

Range checks

A numeric value falls within an expected range, an age between 0 and 120, a price greater than 0, a percentage between 0 and 100.

Format checks

A value matches an expected pattern, an email address has an @ and a domain, a phone number has the right number of digits, a date is a real calendar date.

Membership/referential checks

A value is one of a known set of allowed values (a status is "active", "pending", or "closed", nothing else), or a foreign key actually exists in a reference list.

Report, Don't Just Reject

A validation pass that silently drops failing rows loses information about what actually went wrong. A better pattern: run every rule against every row and produce a report of exactly which rule each failing row broke, then decide row by row (or rule by rule) whether to fix, drop, or flag for manual review. This turns "something's wrong with the file" into "row 4,821 has an out-of-range age and row 9,003 has a malformed email," a much more actionable starting point.

Where to Put Validation in Your Workflow

Validate as early as possible, right after receiving a file, before it's merged, transformed, or loaded anywhere. Catching a bad row at the source is a quick fix; catching the same issue after it's been joined into three other datasets means untangling which downstream results it may have affected.

A Minimal Validation Set Worth Running on Almost Any File

Every row has the expected number of columns (catches structural/delimiter problems)
Required columns have no unexpected blanks
Numeric columns contain only numbers, no stray text
Date columns parse as valid dates
Any column intended as a unique key actually has no duplicates

Doing This in How To CSV

The Column Validator tool runs range, format, and membership rules against every row and reports exactly which rule each failure violated. Data Health Check covers a broader automatic pass for type mismatches, nulls, and structural inconsistencies without needing to define custom rules first.

Need to validate a file before it goes live?

Check every row against your rules, not just a sample.

Validate Your Data

Turn this into a saved workflow

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