{"uid":"cap_gcfML3-qsUuXs4v4xdiSC","slug":"transformation-throughput-string-normalisation-two-measure-correlation-d5170af1","name":"Transformation Throughput — String Normalisation + Two-Measure Correlation","description":"Transformation throughput — String normalisation + Two-measure correlation: String normalisation + Two-measure correlation for supplied transformation throughput records (processed_rows, duration_seconds). 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-13-033?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","processed_rows","duration_seconds"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"processed_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"duration_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","processed_rows":10,"duration_seconds":1},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","processed_rows":20,"duration_seconds":2},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","processed_rows":15,"duration_seconds":3},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","processed_rows":30,"duration_seconds":4}],"maxItems":200,"minItems":1}}},"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_NN03U9lUbC0AUEmI1OkMS","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":"Normalises string fields and computes two-measure correlation between processed_rows and duration_seconds for up to 200 transformation throughput records, returning two analytical reports.","exampleAgentPrompt":"I have up to 200 rows of pipeline run data with row_id, dataset_group, date, processed_rows, and duration_seconds — can you normalize the string fields and run a two-measure correlation analysis between processed_rows and duration_seconds, giving me two reports?","exampleUseCases":[{"title":"ETL pipeline throughput correlation audit","prompt":"I have 150 rows of ETL job logs with row_id, group names like 'group-a' and 'group-b', dates, processed row counts, and duration in seconds — can you normalize the group name strings and compute the correlation between processed_rows and duration_seconds so I can see if longer jobs are just processing more data?"},{"title":"Data warehouse batch performance review","prompt":"Here are 80 records from our nightly warehouse batch jobs with dataset_group, date, processed_rows, and duration_seconds — run string normalization on the group labels and give me both correlation reports so I can understand the throughput-to-time relationship."},{"title":"Pipeline regression check across dataset groups","prompt":"I want to check if processing duration has been scaling correctly with row volume across two dataset groups — I have 60 rows with dates, processed_rows, and duration_seconds. Can you normalize the strings and compute the two-measure correlation to see if something's drifted?"}],"resultDescription":"Returns two analytical reports: one covering string normalisation of the dataset_group and related text fields, and one providing a two-measure (Pearson-style) correlation analysis between processed_rows and duration_seconds across the submitted rows, including grouped statistics. No external data is fetched; all computation is performed on the supplied records only.","failureModes":["Missing required fields (row_id, date, processed_rows, duration_seconds) causes validation errors","Submitting more than 200 rows returns an error due to maxItems constraint","Null values in processed_rows or duration_seconds may reduce correlation validity","Improperly formatted dates (not YYYY-MM-DD) will fail pattern validation","Empty row array rejected by minItems constraint","Payment failure or insufficient USDC balance prevents the call from completing"],"whenToPreferThis":"Choose this endpoint when you have a batch of up to 200 transformation throughput records and need both string normalisation and a two-measure statistical correlation between processed_rows and duration_seconds — packaged together in a single low-cost ($0.01 USDC) call with two reports. Prefer this over generic analytics tools when your data is already structured in the expected schema (row_id, dataset_group, date, processed_rows, duration_seconds) and you want lightweight, local, offline computation with no external lookups or side effects.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:44:06.745Z","isFirstParty":false,"canonicalSlug":"transformation-throughput-string-normalisation-two-measure-correlation-d5170af1"}