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Data Quality Toolkit

Validate, clean, compare, and score workflow data without runtime dependencies

Find Missing Values

AI-generated

Summary

Identify which required fields are missing or empty in each input data item based on a comma-separated list of dot path field names.

Inputs

  • Required Field Names (required) — Comma-separated dot paths specifying the fields to check for presence and non-empty values.

Output shape

a list of items each including the original input fields plus a 'dataQuality' object indicating whether all required fields are present ('valid'), and an array 'missingFields' listing any missing or empty required fields.

The operation processes each input item and returns it augmented with dataQuality results. Missing fields are determined by checking if the specified paths do not exist or are empty (null, undefined, or empty string). If all required fields are present and non-empty, 'valid' is true; otherwise, false with missing fields listed.

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