{
  "id": 6489273,
  "title": "dw-validation-utils: 12 DataWeave validation functions, sandbox-verified reference",
  "url": "https://urgent.news/2026/09/09/dw-validation-utils-12-dataweave-validation-functions-sandbox",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-09-09T22:09:56.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/thasha/dw-validation-utils-12-dataweave-validation-functions-sandbox-verified-reference-2o5"
  },
  "original_language": "en",
  "account": "The dw-validation-utils library provides 12 DataWeave validation functions, which have been verified and can be safely used in a sandbox environment. These functions assist in field validation, pattern matching, and bulk payload validation.\n\nThe functions cover various aspects of data validation, including checking if a value is required, verifying the format of an email address, ensuring a string's length meets minimum and maximum requirements, checking if a value falls within a specified range, validating the format of a string against a regular expression, confirming whether a date is in the correct format, determining if a value is one of several allowed options, validating if a string is a UUID, verifying if a string is a URL, and checking if a string is a phone number in E.164 format.\n\nTo use these functions, developers should import the ValidationUtils module at the beginning of their DataWeave scripts. They can then call the desired function and pass the necessary arguments, such as the payload field they wish to validate, along with any required parameters like field names, minimum and maximum lengths, and regular expression patterns.\n\nThe library returns an object containing information about the validation result. This object includes a boolean value indicating whether the validation passed, the specific field that caused an error, and a message describing the error. Developers can parse this returned object to determine if the validation was successful and branch their logic accordingly. By utilizing these functions, developers can ensure that their data meets the required formats and meets predefined constraints before processing, which helps maintain the integrity and reliability of their applications.",
  "summary": "Problem Validate incoming data before processing. docs 12 reusable validation functions for DataWeave 2.x — field validation, pattern matching, and bulk payload validation. docs The table Function Signature Returns / behaviour Source isRequired fun isRequired(val: Any, fieldName: String): Object = returns name as { valid: false, field: \"name\", error: \"name must not be empty\" } , email as { valid:…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}