{"uid":"cap_w9zod_40Cd2cAwzUtfRhg","slug":"transformation-throughput-analytics-record-quality-audit-exact-row-38ea4e69","name":"Transformation Throughput Analytics: Record Quality Audit + Exact Row Deduplication + Adjacent Row Changes","description":"Transformation throughput — Record quality audit + Exact row deduplication + Adjacent row changes: Record quality audit + Exact row deduplication + Adjacent row changes for supplied transformation throughput records (processed_rows, duration_seconds). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-13-217?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_57BGfp46TCxnF5BSY1fUn","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 three local analytical reports on transformation throughput data (processed_rows, duration_seconds): record quality audit, exact row deduplication, and adjacent row change detection — up to 200 rows per call.","exampleAgentPrompt":"Can you run a quality audit, exact deduplication check, and adjacent row change detection on my transformation throughput records? I have rows with fields like date, dataset_group, processed_rows, and duration_seconds — here's my dataset of about 50 rows, and I want the field analyzed to be processed_rows.","exampleUseCases":[{"title":"ETL pipeline quality check","prompt":"I have 120 rows of daily ETL throughput logs with processed_rows and duration_seconds columns — can you run a record quality audit, find any exact duplicate rows, and flag adjacent row changes on the processed_rows field?"},{"title":"Spotting duplicate pipeline runs","prompt":"My data pipeline team suspects some transformation jobs ran twice and created duplicate records. Can you deduplicate these 80 rows of throughput data and also give me a quality audit and adjacent change report for duration_seconds?"},{"title":"Monitoring batch processing anomalies","prompt":"I want to check these 200 rows of transformation throughput records for data quality issues, exact duplicates, and sudden changes between consecutive rows — can you analyze the processed_rows field and give me all three reports?"}],"resultDescription":"Three independent analytical reports: (1) a record quality audit flagging missing, null, or out-of-range values in processed_rows and duration_seconds; (2) an exact row deduplication report identifying fully duplicate rows; (3) an adjacent row changes report highlighting value differences between consecutive rows for the selected field.","failureModes":["More than 200 rows submitted — request will be rejected or truncated","Missing required fields (row_id, date, dataset_group, processed_rows, duration_seconds) in any row causes validation failure","Invalid date format (not YYYY-MM-DD) causes schema rejection","Invalid field enum value (not 'processed_rows' or 'duration_seconds') causes error","Empty rows array (minItems: 1 not met) causes rejection"],"whenToPreferThis":"Choose this endpoint when you need three combined local analytical operations on transformation throughput data in a single call: quality auditing, deduplication, and adjacent change detection. It is ideal for data engineers who want fast, offline analysis of pipeline metrics without requiring external lookups or live data connections. Prefer it over separate single-report endpoints when you need all three reports at once for up to 200 rows at minimal cost ($0.01 USDC).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:41:51.064Z","isFirstParty":false,"canonicalSlug":"transformation-throughput-analytics-record-quality-audit-exact-row-38ea4e69"}