{"uid":"cap_aCCy84inLrQCdJMNNPQtb","slug":"transformation-throughput-analytics-string-normalisation-date-by-3ab2847b","name":"Transformation Throughput Analytics: String Normalisation + Date by Category Pivot","description":"Transformation throughput — String normalisation + Date by category pivot: String normalisation + Date by category pivot 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-13-035?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},"field":{"enum":["processed_rows","duration_seconds"],"type":"string","default":"processed_rows"}}},"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_jSw0GfempwOrIia-SBvcL","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":"Performs string normalisation and date-by-category pivot analysis on transformation throughput records (processed_rows, duration_seconds) for up to 200 rows, returning two analytical reports.","exampleAgentPrompt":"Can you run string normalisation and a date-by-category pivot on my transformation throughput data? I have up to 50 rows with dataset_group, date, processed_rows, and duration_seconds — I want to analyze the processed_rows field grouped by category across dates.","exampleUseCases":[{"title":"ETL pipeline throughput review","prompt":"I have logs from our nightly ETL jobs — each row has a date, a dataset_group like 'orders' or 'customers', and values for processed_rows and duration_seconds. Can you normalize the string fields and pivot processed_rows by date and category so I can see performance trends across groups?"},{"title":"Data quality audit by group","prompt":"I need to clean up inconsistent dataset_group labels in my transformation records and also get a pivot showing how duration_seconds varied by category over the past month — here are my 120 rows with row_id, date, dataset_group, and duration_seconds."},{"title":"Pipeline comparison across teams","prompt":"We have throughput records from three different teams (dataset_group = 'team-alpha', 'team-beta', 'team-gamma') with dates and processed_rows counts. Can you normalize the group names and produce a date-by-category pivot on processed_rows so we can compare output across teams?"}],"resultDescription":"Returns two analytical reports: (1) a string normalisation report cleaning and standardising the dataset_group and related string fields, and (2) a date-by-category pivot table showing the selected metric (processed_rows or duration_seconds) broken down by date and dataset_group category. Both reports are computed locally from the supplied data with no external lookups.","failureModes":["More than 200 rows submitted — request will be rejected or truncated","Invalid date format (must match YYYY-MM-DD pattern) causes validation error","Missing required fields (row_id, dataset_group, date, processed_rows, duration_seconds) causes schema rejection","Null values in numeric fields may affect aggregation results","Invalid field enum value (not 'processed_rows' or 'duration_seconds') causes error","Empty rows array rejected (minItems: 1)"],"whenToPreferThis":"Choose this endpoint when you have transformation throughput records with dataset_group and date dimensions and need both string normalisation and a date-by-category pivot in a single low-cost call. Ideal for ETL monitoring, pipeline QA, and batch job analysis where you want two complementary analytical views (normalisation + pivot) on up to 200 rows without needing live data lookups or external predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:42:12.258Z","isFirstParty":false,"canonicalSlug":"transformation-throughput-analytics-string-normalisation-date-by-3ab2847b"}