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Dataset Storage Analytics — Daily Sum Series & Date-by-Category Pivot
x402
Dataset Storage Analytics — Daily Sum Series & 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 2026-10-19).
Computes two analytical reports on dataset storage records: a daily sum time series and a date-by-category pivot table, for up to 200 supplied rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Dataset storage — Daily sum series + Date by category pivot: Daily sum series + Date by category pivot for supplied dataset storage records (used_mb, row_count). 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) a daily sum time series showing totals of the selected metric (used_mb or row_count) aggregated per date, and (2) a date-by-category pivot table showing the selected metric broken down by dataset_group for each date. Both reports are computed locally from the supplied row data (no external lookups).
Example use cases
Storage growth trend by day
“I have a list of dataset storage records with dates and used_mb values across different dataset groups — can you give me a daily sum series so I can see how total storage is growing over time? Use the used_mb field.”
Category breakdown pivot table
“I need a date-by-category pivot of my dataset storage records — I want to see used_mb broken down by dataset_group for each date so I can compare how different groups consume storage across days.”
Row count analytics for dataset audit
“Can you run both a daily sum series and a pivot table on my dataset storage data using the row_count field? I have about 150 rows with dates and groups, and I want to spot which days and categories have the highest row counts.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-18-143?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have raw dataset storage records (with dates, groups, and numeric metrics) and need two pre-built analytical views — a daily aggregate time series and a cross-tabulated category pivot — in a single low-cost call. Ideal for exploratory storage analytics on up to 200 rows where you supply the data directly and need no live data fetch, prediction, or external enrichment.
Side effects
- Input array exceeds 200 rows — request will be rejected
- Missing required fields (row_id, dataset_group, date, used_mb, row_count) cause validation errors
- Invalid date format (must be YYYY-MM-DD) results in a schema validation error
- Null values in the selected metric field may affect aggregation totals
- Empty input array (minItems: 1 not met) returns an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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