CSV Validation Guide: Schema Checks, Type Validation, and Error Reporting
Validate CSV files before processing: check column count, enforce types, flag nulls, and report row-level errors with line numbers.
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Tags: data, csv, validation
CSV Validation Guide: Schema Checks, Type Validation, and Error Reporting Parsing a CSV is easy. Trusting what's in it is a different problem. Production CSV pipelines fail in predictable ways: a required column is missing, a numeric field contains a string, a date is formatted differently than expected, or duplicate rows cause downstream constraint violations. This guide shows you how to validate CSV data systematically — schema first, then types, then values — with error reporting that gives a human or a retry mechanism enough context to fix the problem. Validation Layers Good CSV validation happens in four layers: File-level checks — encoding, parse success, non-empty Schema checks — required columns present, no unexpected columns Type validation — each column's values match the…
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