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Transformation Throughput Analytics: Record Quality Audit + String Normalisation + Date by Category Pivot
x402
Transformation Throughput Analytics: Record Quality Audit + String Normalisation + Date by Category Pivot is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-28).
Runs three analytical reports (record quality audit, string normalisation, and date-by-category pivot) on transformation throughput records containing processed_rows and duration_seconds fields, for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Transformation throughput — Record quality audit + String normalisation + Date by category pivot: Record quality audit + String normalisation + Date by category pivot for supplied transformation throughput records (processed_rows, duration_seconds). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.
Output
Three independent analytical reports: (1) a record quality audit flagging issues such as nulls, out-of-range values, and structural problems; (2) a string normalisation report cleaning and standardising text fields like dataset_group; and (3) a date-by-category pivot summarising the selected numeric field (processed_rows or duration_seconds) aggregated by date and dataset_group — all derived locally from the supplied data with no external lookups.
Example use cases
ETL pipeline quality audit
“I've got a batch of 80 transformation throughput records with row IDs, dataset groups, dates, and processed_rows counts — can you audit their data quality, normalise the string fields, and give me a pivot of processed_rows by date and category?”
Duration analysis across pipeline groups
“Run the three analytics reports on my 120 pipeline run records — I want to focus on duration_seconds, check for quality issues, normalise the dataset group names, and see a date-by-category breakdown.”
Data cleaning for transformation logs
“I have transformation logs from last month with fields row_id, dataset_group, date, processed_rows, and duration_seconds — can you do a string normalisation pass and record quality audit, plus pivot processed_rows by date and group, for about 40 rows?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-13-206?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three combined analytical reports — quality audit, string normalisation, and date-by-category pivot — on transformation throughput data in a single low-cost call. It is ideal for data engineers who want a quick, bundled analysis of ETL pipeline records (processed_rows, duration_seconds) without setting up external tooling. Prefer it over general-purpose analytics when your data fits the schema (up to 200 rows with the five required fields) and you need all three report types simultaneously for a flat $0.01 USDC fee.
Side effects
- More than 200 rows submitted — request rejected or truncated
- Missing required fields (row_id, dataset_group, date, processed_rows, duration_seconds) — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Null or out-of-range numeric values may affect pivot and quality report accuracy
- Invalid field enum value (not processed_rows or duration_seconds) — rejected request
- Payment of 0.01 USDC not included or declined — request not processed
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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