Data Validator

Getting Started

The Data Validator helps you validate Excel data files against custom YAML validation rules. Upload your data file and rules, then get instant validation feedback with detailed error reporting.

Quick Start

1. Download the sample files below to understand the format
2. Prepare your Excel data file with column headers
3. Create a YAML rules file defining validation requirements
4. Upload both files and click "Start Validation"
5. Review results and export validated data

Excel File Format

Your Excel file should have a header row with column names, followed by data rows.

Requirements:

• First row must contain column headers
• Column names should match the display_name in your YAML file
• Maximum file size: 10MB
• Supported formats: .xlsx, .xls

Example Excel Structure:

Creating YAML Validation Rules

The YAML file defines validation rules for each column in your Excel file.

Basic Structure:

Field Properties:

• display_name: The exact column header in your Excel file
• type: Data type - string, number, date, email
• required: true or false - whether the field must have a value
• min_length/max_length: For string fields, minimum and maximum character length
• min_value/max_value: For number fields, minimum and maximum values
• pattern: Regular expression pattern the value must match
• allowed_values: List of permitted values (enum)

Example with Allowed Values:

Number Field Example:

Creating Your Own YAML File:

1. List all column headers from your Excel file
2. For each column, create a field entry with an internal key (use underscores, no spaces)
3. Set the display_name to match your Excel column header exactly
4. Choose appropriate type: string, number, date, or email
5. Set required: true for mandatory fields, false for optional
6. Add constraints like min_length, max_length, pattern, or allowed_values as needed
7. Save the file with .yaml or .yml extension

Best Practices

• Start with sample data - test with 5-10 rows before validating large datasets
• Use clear, descriptive column names in your Excel file
• Add validation rules incrementally - start simple, then add more constraints
• Use patterns (regex) for structured data like employee IDs, phone numbers, etc.
• Set appropriate min/max values to catch data entry errors
• Use allowed_values for fields with limited options (departments, status, etc.)
• Review error messages carefully - they indicate exactly what failed validation
• Export validated results to track which records passed/failed

Common Patterns

Email Validation:

Phone Number (Singapore):

Postal Code: