Dataset Quality Checker — Validate Apify Datasets

Validate any Apify dataset for data quality issues. Check for invalid emails, broken URLs, bad phone numbers, duplicate records, and missing fields. Get a quality score and actionable report.

The Problem

You ran an Apify Actor and got a dataset with 10,000 records. But datasets can contain:

• Missing emails
• Duplicate records
• Invalid URLs
• Bad phone numbers
• Empty fields

This Actor scans your dataset and tells you exactly what's wrong.

Example Output

DATA QUALITY REPORT

Dataset: abc123XY
Records: 10,000

Duplicates:          312
Invalid emails:       48
Invalid URLs:        121
Invalid phones:       35
Missing names:        87
Empty records:         3
Total issues:        606

Quality score: 91/100 (Grade: A)
Processing time: 1.2s

How It Works

Apify Dataset ID
      ↓
Load all records
      ↓
Validate emails (RFC 5322)
Validate URLs (domain + TLD)
Validate phones (E.164, 7-15 digits)
Detect duplicates (case-insensitive)
Detect missing/empty fields
      ↓
Calculate quality score (0-100)
      ↓
Push report to dataset

Input

{
  "datasetId": "abc123XY",
  "emailFields": ["email", "emails", "contact.email"],
  "urlFields": ["url", "website", "websiteUrl"],
  "phoneFields": ["phone", "phoneNumber", "contact.phone"],
  "nameFields": ["name", "companyName", "businessName"],
  "duplicateCheckFields": ["email", "url", "phone", "name"],
  "maxRecords": 50000
}

Only datasetId is required. All other fields have sensible defaults.

Output

{
  "dataset_id": "abc123XY",
  "total_records": 10000,
  "processed_records": 10000,
  "duplicate_count": 312,
  "invalid_emails": 48,
  "invalid_urls": 121,
  "invalid_phones": 35,
  "missing_names": 87,
  "empty_records": 3,
  "total_issues": 606,
  "quality_score": 91.0,
  "quality_grade": "A",
  "email_issues": [...],
  "url_issues": [...],
  "phone_issues": [...],
  "name_issues": [...],
  "sample_duplicates": [...],
  "sample_invalid_emails": [...],
  "sample_invalid_urls": [...],
  "checked_at": "2026-08-30T18:00:00Z",
  "processing_time_ms": 1200
}

Quality Score

The score is a weighted average:

• Email validity: 25%
• URL validity: 20%
• Phone validity: 15%
• Name completeness: 20%
• Duplicate rate: 20%

Score	Grade	Meaning
95+	A	Excellent data quality
85-94	B	Good, minor issues
70-84	C	Moderate issues, cleanup recommended
50-69	D	Significant quality problems
<50	F	Poor data quality, major cleanup needed

Use Cases

1. Lead generation validation — Check scraped lead data before importing to CRM
2. Pipeline quality monitoring — Validate datasets in automated workflows
3. Pre-import cleanup — Identify issues before data migration
4. Actor output auditing — Verify your Apify Actors produce quality output

Pricing

Pay-per-event: $0.05 per dataset-quality-check event

One event = one complete dataset quality report.

License

MIT