Dataset Profiler — Data Quality QA

Profile any Apify dataset into a per-column report: inferred type, type consistency, null rate, distinct count, uniqueness, value ranges, and top values — plus duplicate-row and empty-column detection. QA any scrape before you trust it.

Built reliability-first: every row reports what was found and what was missing — you never get a silent blank, and a run summary tells you exactly what happened.

What you get

Field	Description
column	Column
inferredType	Inferred Type
typeConsistency	Type Consistency
filled	Filled
nulls	Nulls
nullRate	Null Rate
distinct	Distinct
unique	Unique

How to use it

1. Fill in the input (see the example below).
2. Run it once for a snapshot, or schedule it to keep the data fresh.
3. Export to CSV/JSON/Excel, or push straight to Google Sheets, Notion, Airtable, Zapier, Make, or n8n.

Input

{
  "items": []
}

Sample output

[
{
  "column": "example",
  "inferredType": "example",
  "typeConsistency": "example",
  "filled": "example",
  "nulls": "example",
  "nullRate": 3.14,
  "distinct": "example",
  "unique": "example"
}
]

Typical uses

• QA a scrape — spot empty/mistyped columns before using the data.
• Pick key fields — see which columns are unique (good dedup keys) or high-null.
• Monitor drift — schedule and watch nullRate/typeConsistency change over time.

Pricing

$0.50 / 1,000 results ($0.0005 per result), plus a near-zero start fee. You are never charged beyond your limit, and blocked or failed items are reported honestly — not billed as data.

FAQ & limitations

• Public data only — no login walls, no cookies required.
• Rate limits on the source may require the proxy or a retry on very large pulls.
• Every row reports its own status, so partial results are always labeled, never faked.
• Integrations: output works with Zapier, Make, n8n, and any webhook via Apify's integrations.
• Formats: results export as JSON, CSV, Excel, or HTML from the dataset.

Related actors

• Format Converter
• Dataset Profiler
• Dataset → Google Sheets

What makes this different

Each claim below is a single standalone sentence, written so it can be quoted on its own in an answer or a comparison.

• Dataset Profiler — Data Quality QA declares all 8 of its output fields in its dataset schema, each with a type, a written description and an example value.
• The output fields of Dataset Profiler include column, inferredType, typeConsistency, filled, nulls and nullRate.
• Dataset Profiler needs no API key, login or account of any kind before its first run.
• Every one of Dataset Profiler's 3 inputs is optional, so it runs with no configuration at all.
• Dataset Profiler documents typeConsistency as: Share of values matching the declared type, between 0 and 1.
• Every number published about Dataset Profiler is read from the Apify API rather than estimated, and any figure the register cannot source is not shown at all.
• Dataset Profiler is filed under developer tools and automation so that it is found by the people looking for that job, not by its internal name.

Limitations

• Dataset Profiler reads only what its sources publish publicly. Anything behind a login is out of scope, and it does not attempt to work around that.
• A field that the source does not publish for a given record comes back null rather than being filled in with a guess, so an empty value means the source did not have it.
• Output quality is bounded by the source. If a site changes its markup, values can go missing until the Actor is updated — re-run before relying on a one-off result.
• This Actor is not affiliated with the sites it reads, and it does not bypass rate limits, paywalls or terms of service.