JSON Field Mapping | Clean Structure, Preserved IDs
Danil Molodkin
A new structure without losing identifiers.
3 records became 2 active products. Four fields were mapped. Quantities became numbers; product IDs stayed strings.
Independent demonstration with synthetic product data. AI-assisted processing, not paid client work.
See the complete before and after
Complete synthetic source and output. String product IDs and numeric quantities are checked separately.
The source export used code, label, state and stock. The target needed product_id, product_name, status and quantity, with active products only.
Map fields. Declare types.
Trim whitespace in label and state, filter active records and apply the four agreed field names. Convert the two retained stock values explicitly into numeric quantities. IDs such as 0101 retain their leading zeros as strings. Six text cells were trimmed and one inactive record was filtered out.
Checked against the source
A separate validator derived the expected schema, rows, values and types from the original input. All 9 checks passed, including encoding, change accounting, formula safety and output hash. A deliberately altered output was rejected.
What the handoff contains
Source JSON, formatted UTF-8 result, explicit field mapping and type rules, change report and separate QA report. Recorded SHA-256 hashes link the exact files to the checks.
Prepare the file, then review the import
This demonstrates flat JSON transformation. It does not claim a completed production import, API integration, nested-JSON extraction or commercial uplift. Actual work starts with an agreed target schema and filtering and type rules.
Need a different file structure?
Send a small export and a sample of the required output. I will confirm scope, price and timing before work begins.
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Posted Oct 11, 2026
Synthetic product demo: 3 records to 2, 4 fields renamed, quantities converted explicitly and string IDs preserved. Separate QA passed 9/9 checks.
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