{"uid":"cap_F68gaDsmcG_K0hY1YDjFR","slug":"pipeline-durations-analytics-two-measure-correlation-date-by-category-f549b130","name":"Pipeline Durations Analytics: Two-Measure Correlation + Date-by-Category Pivot","description":"Pipeline durations — Two-measure correlation + Date by category pivot: Two-measure correlation + Date by category pivot for supplied pipeline durations records (run_seconds, processed_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.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-05-182?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","run_seconds","processed_rows"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"run_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"processed_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","run_seconds":10,"dataset_group":"group-a","processed_rows":1},{"date":"2026-10-02","row_id":"row-2","run_seconds":20,"dataset_group":"group-b","processed_rows":2},{"date":"2026-10-03","row_id":"row-3","run_seconds":15,"dataset_group":"group-a","processed_rows":3},{"date":"2026-10-04","row_id":"row-4","run_seconds":30,"dataset_group":"group-b","processed_rows":4}],"maxItems":200,"minItems":1},"field":{"enum":["run_seconds","processed_rows"],"type":"string","default":"run_seconds"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_XaJ6EvRoHAFFpzOjgYEU7","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Runs two local analytical reports on pipeline duration data: a Pearson/rank correlation between run_seconds and processed_rows, plus a date-by-category pivot table, for up to 200 supplied rows.","exampleAgentPrompt":"Can you analyze my pipeline run data and give me both a correlation between run_seconds and processed_rows, and a date-by-group pivot table? Here are my rows: [{\"row_id\":\"r1\",\"dataset_group\":\"ingest\",\"date\":\"2024-03-01\",\"run_seconds\":120,\"processed_rows\":5000},{\"row_id\":\"r2\",\"dataset_group\":\"transform\",\"date\":\"2024-03-01\",\"run_seconds\":240,\"processed_rows\":9800},{\"row_id\":\"r3\",\"dataset_group\":\"ingest\",\"date\":\"2024-03-02\",\"run_seconds\":95,\"processed_rows\":4200}]","exampleUseCases":[{"title":"ETL performance scaling check","prompt":"I have 80 pipeline run records with run_seconds and processed_rows — can you tell me whether longer-running jobs actually process proportionally more rows, and also pivot the durations by date and pipeline group so I can see trends?"},{"title":"Daily batch job audit by team","prompt":"Here are 150 rows of pipeline data across three dataset groups over the last month. Give me a correlation between run time and rows processed, plus a date-by-category breakdown so I can see which groups are slowest on which days."},{"title":"Data quality regression investigation","prompt":"We had a suspected slowdown last week. I've got pipeline run logs with run_seconds and processed_rows for about 60 jobs. Can you correlate the two metrics and pivot by date and dataset_group so I can spot the anomaly?"}],"resultDescription":"Returns two independent analytical reports: (1) a two-measure correlation report showing the statistical relationship (e.g., Pearson r) between run_seconds and processed_rows across the supplied rows, and (2) a date-by-category pivot table aggregating the selected field (run_seconds or processed_rows) grouped by date and dataset_group. All computation is local with no external lookups.","failureModes":["Fewer than 1 row supplied — request rejected","More than 200 rows supplied — exceeds maxItems limit","Missing required fields (row_id, dataset_group, date, run_seconds, processed_rows) on any row","Date field not matching YYYY-MM-DD pattern — schema validation error","All values for a measure are null — correlation cannot be computed","Invalid field enum value (not run_seconds or processed_rows)","Payment not included or insufficient — x402 payment required error"],"whenToPreferThis":"Choose this endpoint when you have up to 200 pipeline execution records and need both a correlation analysis between run duration and row volume AND a date-grouped pivot table in a single call. It is purpose-built for ETL/pipeline performance analytics and runs entirely on supplied data with no external dependencies, making it fast, deterministic, and privacy-safe. Prefer it over general-purpose analytics tools when you want two specific report types (correlation + pivot) for pipeline metrics without standing up infrastructure.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-22T07:04:48.284Z","isFirstParty":false,"canonicalSlug":"pipeline-durations-analytics-two-measure-correlation-date-by-category-f549b130"}