Your scraper finished.
Did your data survive?
A proposed report for comparing two recurring Apify dataset runs—before missing fields, empty prices or duplicate IDs reach your database.
Not available yet. No payment, API token or dataset upload.
Fictional data and report mockup. No comparison has been executed. Fewer records alone do not establish a scraping error.
Check the data contract.
Then investigate the change.
The proposed workflow would compare equivalent runs against your required fields, unique key and thresholds. Each failed check would point to the affected records.
01 / STRUCTURERequired fields
Identify fields that disappeared. Keep optional fields separate.
02 / COMPLETENESSEmpty values
Review changes in nulls, blank strings and missing values using explicit rules.
03 / UNIQUENESSDuplicate IDs
Check the right key for your records, including variants where relevant.
04 / COVERAGERecord counts
Flag unexpected changes without claiming that every drop is an error.
A small price for a recurring check.
$0.05 USD / completed comparison
Proposed limit: 10,000 records combined across both datasets. This price and limit are being tested; they are not an available offer.
Who is this for?
People running recurring scrapers whose downstream database or automation depends on stable output.
- You already have two comparable dataset runs.
- You know which fields and identifiers matter.
- You want repeatable checks with inspectable reasons.
Need only schema validation? Start with Apify’s built-in dataset validation or your existing validation library. This experiment is testing interest in comparisons across runs, not replacing basic validation.