CSV Schema Validator
Validate CSV files against custom schemas to ensure data quality and integrity
Advertisement
Ad blocked by browser
CSV Data
Validation Results
Ready to Validate
Configure your schema on the right, then click "Validate CSV" to check your data against the defined schema.
Validation Options
Schema
id
name
age
status
Examples of Real-World Usage
6 real-world examples
Data Analysts
Validate datasets before analysis to prevent incorrect results from malformed or unexpected data formats.
System Administrators
Verify data format and integrity before importing into databases, CRMs, or other business systems.
Software Developers
Test CSV exports and imports in applications to ensure they meet expected data formats and requirements.
Data Stewards
Enforce data quality standards by validating CSV files against organizational schemas before approving them for use.
ETL Developers
Validate source data before extraction, transformation, and loading processes to prevent pipeline failures.
Quality Assurance Teams
Verify data exports and imports during testing phases to ensure applications handle data correctly.
Key Features
Powerful tools to validate and ensure CSV data quality
Data Validation
Validate CSV data against a customizable schema to ensure data quality and integrity.
Schema Builder
Create and define schemas visually with support for various data types and validation rules.
Detailed Validation
Detect errors like missing required fields, invalid data types, and values outside acceptable ranges.
Easy Data Import
Upload CSV files or paste data directly, with live preview and validation results.
Error Reporting
Get detailed error and warning messages with row and column identification for easy debugging.
Configurable Options
Customize validation with options for header handling, value trimming, and error tolerance.
How to Use
Simple 5-step process
Step 1
Upload or paste your CSV data into the editor.
Step 2
Define your schema using the visual builder or JSON editor.
Step 3
Configure validation options like header handling and value trimming.
Step 4
Click 'Validate CSV' to check your data against the schema.
Step 5
Review validation results and fix any errors or warnings in your data.
Frequently Asked Questions
Everything you need to know about our process, pricing, and technical capabilities.
See Full FAQA CSV schema is a specification that defines the expected structure and data types of columns in a CSV file. It helps ensure that data conforms to expected formats and constraints before processing or importing it.
The validator supports common data types including strings, integers, floating-point numbers, booleans, dates, email addresses, and enums (values from a predefined set of options).
Yes, for numeric fields (integer and float), you can specify minimum and maximum values that the data must fall within.
Fields marked as 'required' in the schema must be present in the CSV and cannot be empty. Non-required fields can be missing or empty without causing validation errors.
Yes, for string fields, you can define a regular expression pattern that the values must match, enabling custom validation for formats like phone numbers, postal codes, etc.
By default, extra columns not defined in the schema will generate warnings. You can enable the 'Allow Extra Columns' option to ignore them completely.
You can configure header case handling to be case-sensitive, lowercase, or uppercase to match your schema field names appropriately.
Yes, you can download your schema as a JSON file, which you can later upload or paste back into the tool for future validation tasks.
All validation happens in your browser – your CSV data and schema are never sent to any server, ensuring complete data privacy and security.
Yes, you can download a detailed validation report that lists all errors and warnings, which is useful for documenting data quality issues or sharing with team members.
Still have questions?
Can't find what you're looking for? We're here to help you get the answers you need.