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Data Pipeline Runs – Adjacent Row Changes
x402
Data Pipeline Runs – Adjacent Row Changes is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-16).
Computes adjacent (period-over-period) row changes for data pipeline run metrics (processed and rejected rows) across up to 200 supplied records.
Price / call$0.01
Success—
StatusUnknown
How this service works
Data pipeline runs - Adjacent row changes: Adjacent row changes for Data pipeline runs. Up to 200 supplied typed records. Local descriptive analytics; no enrichment or forecast. One of 25 new profiles across 20 shared operations in this industry. 0.01 USDC includes one bounded adjacent row changes report.
Output
A bounded adjacent row changes report showing the period-over-period delta for the selected metric field (processed_rows or rejected_rows) across consecutive records in the supplied pipeline run dataset. Results are local descriptive analytics with no external enrichment or forecasting.
Example use cases
Daily pipeline throughput delta check
“I have daily pipeline run stats for two pipelines over the past week — can you calculate how processed_rows changed between each consecutive run for both group-a and group-b?”
Rejected row spike investigation
“My pipeline had some spikes in rejected rows recently. Can you run an adjacent row change analysis on rejected_rows for these 10 pipeline run records so I can see where the biggest jumps occurred?”
ETL monitoring dashboard data
“For my ETL monitoring dashboard, I need period-over-period changes in processed_rows across 50 pipeline run records I'll supply — give me the adjacent row change report so I can visualize the deltas.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ar-analytics-pipeline-runs-period-change?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, local descriptive analytics comparing consecutive pipeline run records — specifically adjacent period deltas for processed or rejected row counts — without requiring external data enrichment or forecasting. Ideal for ETL monitoring, pipeline health dashboards, and data quality audits where you supply your own records (up to 200) and want per-row change calculations at low cost ($0.01 USDC).
Side effects
- Fewer than 1 row supplied returns an error (minItems:1 required)
- More than 200 rows supplied exceeds the limit (maxItems:200)
- Invalid date format (must match YYYY-MM-DD pattern) causes validation failure
- Invalid field enum value (must be 'processed_rows' or 'rejected_rows') causes rejection
- Missing required fields (row_id, pipeline, date, processed_rows, rejected_rows) cause schema validation errors
- Numeric values outside ±1,000,000,000,000 are rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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