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Validation commands enable you to validate data quality, structure, and content to ensure data meets expected criteria before processing. These commands are essential for data quality assurance and error prevention.

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Overview

Validation commands provide functionality for:
  • Validating data structure and format
  • Checking data types and values
  • Verifying data completeness
  • Validating data against schemas
  • Ensuring data quality standards

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Common Use Cases

  • Data Quality Assurance: Ensure data meets quality standards
  • Schema Validation: Validate data against expected schemas
  • Type Checking: Verify data types match expectations
  • Completeness Checks: Ensure required fields are present
  • Error Prevention: Catch data issues before processing

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Command Parameters

Validation commands typically support the following parameters:
ParameterTypeDescriptionRequired
dataobjectData to validateYes
schemaobjectValidation schemaYes
rulesarrayValidation rules to applyYes
onErrorstringAction to take on validation errorNo

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Validation Types

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Structure Validation

Validate data structure: 
{
  "command": "validate",
  "params": {
    "data": "${input}",
    "schema": {
      "type": "object",
      "required": ["id", "name"],
      "properties": {
        "id": {"type": "string"},
        "name": {"type": "string"}
      }
    }
  }
}

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Type Validation

Validate data types: 
{
  "command": "validate-type",
  "params": {
    "data": "${value}",
    "expectedType": "string"
  }
}

​
Value Validation

Validate data values: 
{
  "command": "validate-value",
  "params": {
    "data": "${value}",
    "rules": [
      {"type": "minLength", "value": 5},
      {"type": "maxLength", "value": 100}
    ]
  }
}

​
Validation Rules

Common validation rules include:
  • Required Fields: Ensure required fields are present
  • Type Checking: Verify data types match expectations
  • Range Validation: Check numeric values are within ranges
  • Format Validation: Validate string formats (email, URL, etc.)
  • Length Validation: Check string lengths

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Best Practices

  • Validate Early: Validate data as early as possible in workflows
  • Clear Error Messages: Provide clear, actionable error messages
  • Handle Validation Errors: Implement proper error handling
  • Use Schemas: Define validation schemas for consistency
  • Test Validation: Test validation rules with various data scenarios