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Data Validation Node

The Data Validation Node checks incoming data against a JSON Schema before passing it to downstream nodes. This ensures only well-structured, expected data flows through your pipeline, reducing errors and improving reliability.

Data Validation Node

Configuration​

Enter a JSON Schema to validate incoming data.

Example
{
    "type": "object",
    "properties": {
        "products": {
        "type": "array",
        "items": {
            "type": "object",
            "required": ["id", "title", "price"],
            "properties": {
            "id": { "type": "integer" },
            "title": { "type": "string" },
            "price": { "type": "number" }
            }
        }
        }
    },
    "required": ["products"]
}

Data Validation Node Params

Example Usage​

For example usage, see the Product Data Validation and Visualization workflow, which demonstrates fetching API data, validating it, and visualizing the results.

Common Use Cases​

  1. API Response Validation: Ensure third-party API responses match expected formats.
  2. Input Sanitization: Prevent malformed or incomplete data from entering your workflow.
  3. Data Quality Assurance: Enforce required fields and data types.
  4. Error Prevention: Catch issues early and provide feedback for correction.

Best Practices​

  • Test your schema: Use tools like JSON Schema Validator to verify your schema.
  • Be specific: Define required fields and types to catch subtle errors.
  • Handle errors: Use downstream nodes to handle validation failures gracefully.
  • Keep schemas up to date: Update schemas as your data structures evolve.

Troubleshooting​

Common Issues​

  • Invalid schema: Ensure your schema is valid JSON and follows the JSON Schema standard.
  • Unexpected validation errors: Double-check that incoming data matches the schema.
  • Empty or missing fields: Use required properties to enforce presence of important fields.

Performance Tips​

  • Use for small to medium-sized data objects.
  • For large datasets, validate only the necessary parts to improve performance.
  • Test with sample data before running full workflows.

Supported Data Formats​

  • Input: Any JSON object or array.
  • Output: The original data if validation passes; an error message if validation fails.

Why Use Data Validation?​

Validating data before processing ensures that only well-structured, expected data flows through your pipeline. This reduces errors, improves reliability, and makes debugging easier—especially when working with third-party APIs.