{"uid":"cap_K4I4HsGPjy7mkr1p04k6c","slug":"transformation-throughput-exact-row-deduplication-trailing-mean-series-63806d0b","name":"Transformation Throughput — Exact Row Deduplication + Trailing Mean Series","description":"Transformation throughput — Exact row deduplication + Trailing mean series: Exact row deduplication + Trailing mean series 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-13-045?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"},"window":{"type":"integer","default":2,"maximum":200,"minimum":2}}},"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_02GUxV4ziBEwruq4lBS7p","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":"Deduplicates transformation throughput records exactly and computes trailing mean series for processed_rows or duration_seconds fields across up to 200 rows.","exampleAgentPrompt":"Can you deduplicate my transformation throughput records and compute a trailing mean series with a window of 3 for processed_rows? Here are my rows: row-1 on 2026-10-01 in group-a with 100 processed rows and 5 seconds, row-2 on 2026-10-02 in group-b with 200 rows and 10 seconds, and row-3 on 2026-10-03 in group-a with 100 rows and 5 seconds.","exampleUseCases":[{"title":"ETL pipeline duplicate cleanup","prompt":"I have 150 rows of daily ETL run records with processed_rows and duration_seconds — can you deduplicate them exactly and also give me a trailing mean series with a window of 5 for processed_rows so I can spot trends?"},{"title":"Batch job throughput trend analysis","prompt":"Run a trailing mean analysis with a window of 7 on duration_seconds for my transformation throughput logs, and clean out any exact duplicate rows first — I have about 80 records across three dataset groups."},{"title":"Data quality audit for transformation logs","prompt":"I need two reports on my pipeline throughput data: first deduplicate the 60 rows I have, then compute a rolling mean of processed_rows using a window of 4 to see if throughput is improving over time."}],"resultDescription":"Two analytical reports: (1) an exact row deduplication report identifying and removing fully duplicate records from the supplied transformation throughput rows, and (2) a trailing mean series computed over the specified numeric field (processed_rows or duration_seconds) using the given rolling window size — all derived locally from the supplied data with no external lookups.","failureModes":["Input array is empty or missing — returns validation error","More than 200 rows supplied — request rejected","Invalid date format (not YYYY-MM-DD) — schema validation failure","Field enum value not one of processed_rows or duration_seconds — validation error","Window value outside range 2–200 — schema rejection","All rows are unique — deduplication report returns empty duplicate set"],"whenToPreferThis":"Choose this endpoint when you need both exact row-level deduplication and a trailing/rolling mean time series computed together in a single API call on transformation throughput data (processed_rows, duration_seconds). It is ideal for data engineers who want to clean pipeline run logs and simultaneously analyze throughput trends without writing custom code. Prefer this over general analytics platforms when you need a lightweight, pay-per-call solution for up to 200 records.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:41:48.828Z","isFirstParty":false,"canonicalSlug":"transformation-throughput-exact-row-deduplication-trailing-mean-series-63806d0b"}