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Apify Dataset Quality Auditor

Transform apify dataset quality auditor inputs into structured rows, clear errors, confidence signals, and automation-ready output.

Who this is for

Developers, analysts, data operations teams, AI-agent builders, and automation owners use this actor when they need focused apify dataset quality auditor output instead of a broad generic scraper or manual checking.

Buyer outcomes

• Turn apify dataset quality auditor inputs into repeatable structured output for downstream systems.
• Prioritize cleanup with schema, quality, extraction, change, warning, and error fields.
• Route normalized rows into Apify datasets, APIs, spreadsheets, automations, or AI-agent workflows.

Sources monitored

• Apify datasets/storage

Inputs

• sourceMode: use sample for a smoke run, startUrls for URL-backed PDFs/datasets/pages, or configured dataset modes.
• startUrls: PDF URLs, dataset URLs, public files, or pages to parse, audit, normalize, extract, or compare.
• sourceIds: approved source or dataset identifiers used to scope the run.
• maxItems: bounded number of files, tables, rows, fields, or changes to process.
• watchlistTerms: optional column names, schema keys, quality rules, or extraction terms.
• webhookUrl: optional completion destination for the transformation report.
• outputMode: use sample records for Store validation or production output for normal runs.

How it transforms the input

• Input: PDF, CSV, JSON, Apify dataset URL, table-like document, website, or messy operational data.
• Transformation: parse, extract, normalize, audit, compare, dedupe, or report schema/quality issues.
• Output: normalized fields, extracted tables/rows, schema report, diff report, warnings, confidence, and errors.

Outputs

The actor returns structured transformation records: extracted tables, normalized schemas, dataset quality metrics, diff reports, parsed fields, warnings, errors, and confidence signals.

Family-specific fields to expect:

• extractedRows: Rows parsed or produced by the transformation.

• schema: Detected, normalized, or target schema.

• columns: Detected table or dataset columns.

• validationErrors: Validation, parse, schema, or quality errors.

• duplicateCount: Duplicate rows or keys found during audit/dedupe.

• nullRate: Null or empty-value rate for important fields.

• changedRecords: Added, removed, or changed records for diff workflows.

• recordId: Stable record ID for exports, dedupe, and downstream joins.

• title: Human-readable record title for review and export.

• sourceName: Source identifier used to trace where the record came from.

• sourceUrl: Direct source URL for review and audit.

• dedupeKey: Stable key used for delta mode and duplicate suppression.

• retrievedAt: Timestamp showing when the actor retrieved or generated this record.

• score: Normalized field for filtering, routing, or downstream review.

• scoreReasons: Buyer-readable explanation for the score or match.

• confidence: Normalized field for filtering, routing, or downstream review.

• errors: Normalized field for filtering, routing, or downstream review.

• runSummary: Run-level summary for counts, filters, charges, and next actions.

Pricing

This actor uses Apify pay-per-event pricing. Current public listing guidance: $29-$49 / 1,000 launch validation records until public data proof is complete. Charges are tied to buyer-visible value events such as qa-report-created, row-audited, issue-found, dataset-processed, record-saved, enriched-record. Small validation runs are supported so you can inspect output before scaling a schedule.

• qa-report-created: Charge after producing one dataset QA report. Typical price: $0.180. A run that produces 10 matching records charges only for the matched buyer-value events and remains capped by the run limit.
• row-audited: Charge after producing one row audited. Typical price: $0.001. A run that produces 10 matching records charges only for the matched buyer-value events and remains capped by the run limit.
• issue-found: Charge after producing one actionable quality issue. Typical price: $0.004. A run that produces 10 matching records charges only for the matched buyer-value events and remains capped by the run limit.
• dataset-processed: Base charge when Apify Dataset Quality Auditor writes a non-empty default dataset. Typical price: $0.011. A run that produces 10 matching records charges only for the matched buyer-value events and remains capped by the run limit.
• first-run-cap: Recommended first run budget cap. Typical price: $2.000. Start with the default small run, inspect the dataset, then raise maxItems or schedule recurring runs.

API example

curl-X POST "https://api.apify.com/v2/actors/zentrafoundry~apify-dataset-quality-auditor/runs"\
+ -H"Authorization: Bearer $APIFY_TOKEN"\
+ -H"Content-Type: application/json"\
+ -d'{"maxItems":10,"sourceIds":["APIFY-DATASETS"],"includeSourceUrls":true,"includeMatchReasons":true,"outputMode":"buyer-ready-records"}'

Recommended first run

{
"maxItems":10,
"sourceIds":[
"APIFY-DATASETS"
],
"includeSourceUrls":true,
"includeMatchReasons":true,
"outputMode":"buyer-ready-records"
}

Sample output

Sample status: sample_unavailable at https://zentra.nimblique.studio/external/actor-review/samples/apify-dataset-quality-auditor.json. No fake sample is published; run a bounded real sample refresh before using examples in promotion.

Recommended public tasks

[
{
"name":"Validate one small data transformation",
"description":"Low-cost validation run for checking parsed, normalized, audited, or diffed output.",
"input":{
"maxItems":10,
"sourceIds":[
"APIFY-DATASETS"
],
"includeSourceUrls":true,
"includeMatchReasons":true,
"outputMode":"buyer-ready-records",
"actorSlug":"apify-dataset-quality-auditor"
}
},
{
"name":"Recurring dataset utility check",
"description":"Recurring batch for schema, quality, extraction, or change reports.",
"schedule":"Daily during local business hours",
"input":{
"maxItems":25,
"sourceIds":[
"APIFY-DATASETS"
],
"includeSourceUrls":true,
"includeMatchReasons":true,
"outputMode":"buyer-ready-records",
"actorSlug":"apify-dataset-quality-auditor"
}
}
]

Use cases

• Clean, extract, compare, or audit apify dataset quality auditor data before it enters a downstream workflow.
• Convert messy inputs into predictable JSON/CSV-ready rows for APIs, spreadsheets, or agents.
• Surface schema drift, duplicates, nulls, errors, warnings, or changed records.
• Use small validation runs before connecting larger datasets or destinations.

Trust and compliance

• Uses Apify datasets/storage.
• Keeps source URLs and source identifiers in output records for auditability.
• Does not require private credentials unless a source is explicitly configured for approved authenticated access.

Limitations

• Results depend on public-source availability, source uptime, and source update cadence.
• Public sources can revise records after publication; rerun scheduled tasks for fresh evidence.
• Scores and match reasons are decision-support signals, not legal, financial, procurement, medical, safety, or regulatory advice.
• Large production runs can cost more than the default smoke run; start small, inspect output, then scale schedules.

FAQ

Can I run this without URLs? Yes. The default sample mode is designed to succeed without user-supplied URLs, and URL-backed runs can use startUrls when needed.

Can I schedule it? Yes. Use sinceLastRun, watchlistTerms, and optional webhookUrl to turn the actor into a recurring alert or report workflow.

How do I verify value before scaling? Run the recommended first-run input, review the sample output fields, then increase maxItems or schedule recurring runs after the dataset matches your use case.