{"uid":"cap_c3XJSACS3bIU3loxhRnY-","slug":"data-ingestion-batches-adjacent-row-changes-two-measure-correlation-2776e829","name":"Data Ingestion Batches — Adjacent Row Changes + Two-Measure Correlation","description":"Data ingestion batches — Adjacent row changes + Two-measure correlation: Adjacent row changes + Two-measure correlation for supplied data ingestion batches records (received_rows, accepted_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-01-158?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","received_rows","accepted_rows"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"accepted_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"received_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","accepted_rows":1,"dataset_group":"group-a","received_rows":10},{"date":"2026-10-02","row_id":"row-2","accepted_rows":2,"dataset_group":"group-b","received_rows":20},{"date":"2026-10-03","row_id":"row-3","accepted_rows":3,"dataset_group":"group-a","received_rows":15},{"date":"2026-10-04","row_id":"row-4","accepted_rows":4,"dataset_group":"group-b","received_rows":30}],"maxItems":200,"minItems":1},"field":{"enum":["received_rows","accepted_rows"],"type":"string","default":"received_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_f6ZZd-IQ2Z9BS1lGLqKzE","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":"Analyzes up to 200 data ingestion batch records to produce two reports: adjacent row-to-row change detection and Pearson/correlation analysis between received_rows and accepted_rows.","exampleAgentPrompt":"Analyze my ingestion batch data for adjacent row-to-row changes and correlate received_rows against accepted_rows — here are my records: [{\"row_id\":\"r1\",\"dataset_group\":\"orders\",\"date\":\"2024-01-01\",\"received_rows\":1000,\"accepted_rows\":980},{\"row_id\":\"r2\",\"dataset_group\":\"orders\",\"date\":\"2024-01-02\",\"received_rows\":1050,\"accepted_rows\":900}] and use received_rows as the primary field.","exampleUseCases":[{"title":"ETL pipeline quality monitoring","prompt":"I have daily ingestion batch logs for the past month across two dataset groups — can you run adjacent row change detection and a correlation analysis between received_rows and accepted_rows to see if there are any suspicious drops or mismatches? Here's my data as a list of rows with row_id, dataset_group, date, received_rows, and accepted_rows."},{"title":"Data acceptance rate trend analysis","prompt":"We process data from three upstream sources and I want to understand how closely accepted_rows tracks received_rows over time. Can you correlate those two measures and also flag any big jumps between consecutive rows? I'll supply about 150 records with row_id, dataset_group, date, and both row counts."},{"title":"Ingestion anomaly investigation","prompt":"Something looks off in our ingestion pipeline last week — can you compute the adjacent row changes for received_rows and also check the correlation between received_rows and accepted_rows across these 40 batch records so I can spot where the problem started?"}],"resultDescription":"Returns two analytical reports: (1) an adjacent row changes report showing sequential deltas between consecutive rows for the selected field (received_rows or accepted_rows), and (2) a two-measure correlation report quantifying the statistical relationship between received_rows and accepted_rows across the supplied dataset. No external data is fetched; analysis is performed locally on the supplied rows.","failureModes":["Fewer than 1 row supplied — minimum 1 row required","More than 200 rows supplied — batch size limit exceeded","Missing required fields (row_id, dataset_group, date, received_rows, accepted_rows) — validation error","Invalid date format (must match YYYY-MM-DD pattern) — schema rejection","Non-numeric values in received_rows or accepted_rows — type error","Payment not completed via x402 protocol — 402 response, no analysis returned"],"whenToPreferThis":"Choose this endpoint when you have up to 200 data ingestion batch records and need both sequential change detection (adjacent row deltas) and a correlation analysis between received_rows and accepted_rows in a single $0.01 call. Prefer this over generic stats APIs when your data is already structured as ingestion pipeline logs with dataset_group and date fields. Not suitable for live pipeline monitoring, prediction, or datasets larger than 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-17T00:47:13.999Z","isFirstParty":false,"canonicalSlug":"data-ingestion-batches-adjacent-row-changes-two-measure-correlation-2776e829"}