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Dataset Partition Sizes — Adjacent Row Changes + Date by Category Pivot Analytics
x402
Dataset Partition Sizes — Adjacent Row Changes + Date by Category Pivot Analytics 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).
Runs two analytical reports on supplied dataset partition size records: adjacent row-over-row change detection and a date-by-category pivot, for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Dataset partition sizes — Adjacent row changes + Date by category pivot: Adjacent row changes + Date by category pivot for supplied dataset partition sizes records (row_count, file_mb). 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 differences in the selected metric (row_count or file_mb) between consecutive records, and (2) a date-by-category pivot table breaking out the metric values by date and dataset_group — all computed locally from the supplied data, no external lookups.
Example use cases
Detecting partition size spikes over time
“Here are my dataset partition logs with dates, group names, row counts, and file sizes — can you run the adjacent row changes report on file_mb so I can spot any sudden spikes between consecutive records?”
Pivot partition metrics by group and date
“I have partition size records for groups A and B across several dates — can you generate the date-by-category pivot for row_count so I can see how each group compares day by day?”
Auditing dataset growth across pipeline stages
“I need to audit how our dataset partitions grew across pipeline runs — please run both the adjacent row changes and date-by-category pivot reports on these records using the row_count field.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-07-160?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have raw dataset partition size records and need two specific analytical views — sequential row-to-row change detection and a date-by-category pivot — in a single low-cost call. It is ideal for data engineers or pipelines monitoring partition growth trends without needing external data sources or predictive models. Best for structured tabular inputs of up to 200 rows with date, group, and numeric size fields.
Side effects
- Missing required fields (row_id, dataset_group, date, row_count, file_mb) cause validation errors
- Date strings not matching YYYY-MM-DD pattern are rejected
- Input arrays exceeding 200 rows are rejected
- Null numeric values in the selected field may produce incomplete or skipped change calculations
- Invalid field enum value (not 'row_count' or 'file_mb') causes a schema error
- Empty rows array (minItems: 1) is rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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