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Join Coverage Analytics: Date-Category Pivot + Keyed Dataset Comparison
x402
Join Coverage Analytics: Date-Category Pivot + Keyed Dataset Comparison is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).
Analyzes join coverage data (matched_rows, source_rows) by producing a date-by-category pivot table and a keyed dataset comparison report for two supplied datasets, up to 200 rows each.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Date by category pivot + Keyed dataset comparison: Date by category pivot + Keyed dataset comparison for supplied join coverage records (matched_rows, source_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 independent analytical reports: (1) a date-by-category pivot table showing matched_rows or source_rows values broken out by date and dataset_group, and (2) a keyed dataset comparison report contrasting the two supplied datasets row by row using the selected field. Both reports are computed locally from the supplied data with no external lookups.
Example use cases
ETL pipeline join quality audit
“I have two snapshots of my ETL pipeline's join coverage — each up to 200 rows with date, dataset_group, matched_rows, and source_rows — can you pivot both by date and category and compare them keyed by row_id to show where join quality changed between the two runs?”
Dataset version comparison by group
“I ran the same join twice with two different configs and have records for both with matched_rows and source_rows per group and date — can you pivot each by date and dataset_group and then do a keyed comparison using matched_rows to highlight the differences?”
Monthly join coverage trend review
“Here are two months of join coverage records for groups A and B, each with date, matched_rows, and source_rows — can you build a date-by-category pivot and compare the two months side by side keyed by row_id, analyzing the source_rows field?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-188?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have two sets of join coverage records (with matched_rows and source_rows) and need both a date-by-category pivot view and a direct keyed comparison between the two datasets in a single low-cost call. Ideal for data engineers auditing ETL join quality, comparing dataset snapshots, or reviewing how join coverage varies across groups and dates — all without needing external data or live lookups.
Side effects
- More than 200 rows submitted — request rejected
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) — validation error
- Date values not in YYYY-MM-DD format — pattern validation failure
- Numeric values outside allowed range (±1,000,000,000,000) — schema rejection
- Empty rows array — minItems violation
- Invalid field enum value (must be matched_rows or source_rows) — schema error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| other | array |
Tags
x402
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