What is csv validation?
CSV validation is the process of checking CSV (Comma-Separated Values) file data against defined rules to ensure accuracy, consistency, and compliance with expected formats before processing.
Understanding csv validation
CSV validation ensures that data in CSV files meets your quality standards before it enters your system. This includes checking data types (is this field a number?), formats (is this a valid email?), required fields (is this column filled?), and business rules (does this value make sense?).
Validation can happen at multiple levels: - Field-level: Checking individual values (email format, phone number format) - Row-level: Checking relationships between fields in the same row (end date after start date) - File-level: Checking overall file structure (required columns present, no duplicates)
Without proper validation, bad data enters your system and causes downstream problems: failed transactions, incorrect reports, and frustrated users.
Key points
Checks data against predefined rules before processing
Includes type validation, format validation, and business rules
Can be field-level, row-level, or file-level
Prevents bad data from entering your system
Should provide clear error messages for users to fix issues
Frequently asked questions
What types of validation should I apply to CSV files?
Common validations include: required field checks, data type validation (number, date, email), format validation (phone numbers, postal codes), range checks (min/max values), uniqueness checks, and cross-field validation (field A depends on field B).
Should validation happen client-side or server-side?
Ideally both. Client-side validation provides instant feedback and better UX. Server-side validation is essential for security and data integrity. Tools like Ivandt do client-side validation in the browser before data is ever sent to your server.
How do I handle validation errors in bulk uploads?
Best practice is to show errors inline next to the problematic cells, allow users to fix issues directly, provide suggestions where possible (Smart Fix), and let users choose to skip invalid rows or fix everything before proceeding.