3.2.4 Guide to Understanding and Interpreting Validator Output on Databoutique

Updated by Andrea Squatrito

Guide to Understanding and Interpreting Validator Output on Databoutique

When uploading data to Databoutique, sellers must follow specific data formats and structures. Databoutique employs an automated validator to check submissions for compliance with these requirements. Understanding validator output ensures smooth data upload, adherence to the data schema, and prevention of upload failures.

1. Purpose of the Validator

The Databoutique validator confirms that:

  • Data follows the required schema for the specified product type.
  • Mandatory fields and file structure adhere to Databoutique’s guidelines.
  • Uploaded files pass Quality Assurance (QA), ensuring consistency and usability for buyers.

The validator checks the metadata, data format, and field structure to ensure compatibility with Databoutique’s platform and that data quality standards are met.

2. Reading Validator Messages

The validator output provides actionable feedback based on file inspection. Key areas include:

  • File Structure Compliance:
    • Field Names: Ensure each field matches the schema exactly. For example, if a schema requires product_code, a deviation like ProductCode may cause failure.
    • File Name: Databoutique requires files to have a standard name format (e.g., data_file.txt). Ensure the file is properly named according to instructions.
  • Data Format Errors:
    • Field Format: Each field must adhere to specified formats, such as dates in YYYY-MM-DD, currency codes as three-letter ISO codes, and numeric prices with two decimal precision.
    • Semicolon-Separated Values: All fields should be separated by semicolons (;). Errors here may indicate issues with delimiters.
    • Mandatory Fields: Absence of mandatory fields such as contract_id, seller_id, and dbq_prd_type will trigger errors. These fields ensure that each data upload is properly attributed.
  • Content Compliance:
    • Schema-Specific Rules: Ensure adherence to the specified schema, which varies by data type (e.g., E0001 for e-commerce prices). Mismatches in schema requirements lead to errors like “Expected field full_price but found price_full.”
    • Value Integrity: The validator checks for field consistency, such as using recognized ISO codes for countries and currencies, ensuring numeric formats for prices, and verifying correct contract ID usage.

3. Common Validator Output Errors and Solutions

Here are typical validator messages and their interpretation:

  • “File structure mismatch”: The file’s columns or field names do not align with the schema. Double-check field names against the schema for exact matches.
  • “Mandatory field missing: contract_id”: This field is required for identification within Databoutique’s system. Ensure it’s included and correctly filled out.
  • “Invalid format in field competence_date”: The competence_date field must follow the YYYY-MM-DD format. Check the date entries for accuracy.
  • “Unrecognized field”: This means there’s an extra or incorrectly named field in the file. Refer to the schema’s field list to verify that only required fields are present.
  • “Field separator mismatch”: This indicates that the file is not using semicolons as separators. Ensure that semicolons (;) are used consistently.

4. Best Practices for Passing the Validator

  • Field Naming Consistency: Use exact field names as defined in the schema (e.g., product_code instead of prod_code).
  • Correct Formatting: Ensure numeric, date, and text fields match the schema’s specified format.
  • Validate Locally: Use tools like spreadsheet software or scripting in Python or R to validate formats locally before uploading.
  • Check for Missing Values: While some schemas allow optional fields, mandatory fields must always contain data. Use NULL or an appropriate placeholder only if the schema specifies it as acceptable.

5. Handling Validation Errors

After an upload attempt, any validation errors will be provided in a report. Address each error systematically by:

  1. Correcting File Structure: Adjust field names, order, and data types.
  2. Re-checking Mandatory Fields: Verify all required identifiers are present and formatted correctly.
  3. Re-uploading: Once corrections are made, re-upload the file. Allow a few minutes for the validator response.

By carefully interpreting validator output and adhering to schema requirements, you can ensure successful data uploads, maintaining data integrity and reliability for Databoutique users.

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