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Data Ingestion Batches — Adjacent Row Changes + Two-Measure Correlation
x402
Data Ingestion Batches — Adjacent Row Changes + Two-Measure Correlation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-17).
Analyzes up to 200 data ingestion batch records to produce two reports: adjacent row-to-row change detection and Pearson/correlation analysis between received_rows and accepted_rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Data ingestion batches — Adjacent row changes + Two-measure correlation: Adjacent row changes + Two-measure correlation for supplied data ingestion batches records (received_rows, accepted_rows). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Returns two analytical reports: (1) an adjacent row changes report showing sequential deltas between consecutive rows for the selected field (received_rows or accepted_rows), and (2) a two-measure correlation report quantifying the statistical relationship between received_rows and accepted_rows across the supplied dataset. No external data is fetched; analysis is performed locally on the supplied rows.
Example use cases
ETL pipeline quality monitoring
“I have daily ingestion batch logs for the past month across two dataset groups — can you run adjacent row change detection and a correlation analysis between received_rows and accepted_rows to see if there are any suspicious drops or mismatches? Here's my data as a list of rows with row_id, dataset_group, date, received_rows, and accepted_rows.”
Data acceptance rate trend analysis
“We process data from three upstream sources and I want to understand how closely accepted_rows tracks received_rows over time. Can you correlate those two measures and also flag any big jumps between consecutive rows? I'll supply about 150 records with row_id, dataset_group, date, and both row counts.”
Ingestion anomaly investigation
“Something looks off in our ingestion pipeline last week — can you compute the adjacent row changes for received_rows and also check the correlation between received_rows and accepted_rows across these 40 batch records so I can spot where the problem started?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-01-158?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 data ingestion batch records and need both sequential change detection (adjacent row deltas) and a correlation analysis between received_rows and accepted_rows in a single $0.01 call. Prefer this over generic stats APIs when your data is already structured as ingestion pipeline logs with dataset_group and date fields. Not suitable for live pipeline monitoring, prediction, or datasets larger than 200 rows.
Side effects
- Fewer than 1 row supplied — minimum 1 row required
- More than 200 rows supplied — batch size limit exceeded
- Missing required fields (row_id, dataset_group, date, received_rows, accepted_rows) — validation error
- Invalid date format (must match YYYY-MM-DD pattern) — schema rejection
- Non-numeric values in received_rows or accepted_rows — type error
- Payment not completed via x402 protocol — 402 response, no analysis returned
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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