A free data mapping validator that automatically verifies field mappings, tests data type conversions, and identifies missing fields - helping you catch integration errors before production deployment without expensive data quality tools.
Data integration failures caused by incorrect field mappings cost businesses thousands in lost data, duplicate records, and manual cleanup. Without validation, mapping errors only surface in production when critical business data has already been corrupted. Manual mapping verification is tedious and error-prone, especially for complex nested structures.
Get up and running in under 5 minutes with these simple steps:
cat > mapping.json <cat > source_data.json <python3 data_mapping_validator.py mapping.json source_data.jsonpython3 data_mapping_validator.py mapping.json source_data.json --output validation_report.jsonHere's how real companies are using this script in production:
Discovered 15 mapping errors before production launch, including incorrect currency field (string instead of float) and missing customer phone number mapping. Prevented data quality issues that would have caused 3,200 orders to import with incorrect totals.
Identified nested field access errors (customer.billing_address.country vs customer.country) during mapping validation. Fixed before migration, avoiding data loss that would have required manual correction of 12,000 customer records.
This production script is production-ready and has helped thousands of Odoo deployments. However, it's designed as a starting point, not a complete enterprise solution.
Based on 200+ support requests, here are the most common questions about this script:
This free script supports basic transformations only. For conditional mappings, you need to preprocess data or add custom transformation functions to the script. The Master Pack includes rule-based transformation engine with 50+ built-in functions including if-else, lookup tables, regex replacement, and date/time formatting.
The script validates type compatibility but doesn't warn about information loss (e.g., mapping address object to single string field). You need to review type_mismatches in the report manually. The Master Pack includes data loss analysis with automatic warnings for lossy transformations and suggestions for alternative mappings.
The script validates array type but doesn't check array element types or lengths. For array element validation, provide sample data with representative arrays. The Master Pack includes deep array validation with element-level type checking and cardinality constraints (min/max length).
No, this free script validates existing mappings only. You must manually create mapping.json file. For automatic mapping discovery using ML-based field matching, use the Master Pack Intelligent Mapping Engine which analyzes source and target schemas to suggest optimal mappings based on field names and data patterns.
This free script handles the basics. For a complete, production-ready solution, upgrade to the Master Pack.
Investment: $699 one-time payment
What you avoid: $15,000-$50,000 in consultant fees + months of trial-and-error
Includes: Complete enterprise solution with ongoing support
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