CSV Join Quality Checker

Store Positioning

Store title: CSV Join Quality Checker

Short description: Check whether two CSV files are safe to join by analyzing key coverage, null keys, duplicates, type mismatches, whitespace/case drift, cardinality, unmatched rows, and likely row multiplication.

SEO title: CSV Join Quality Checker — data QA, validation, and cleanup utility

SEO description: Check whether two CSV files are safe to join by analyzing key coverage, null keys, duplicates, type mismatches, whitespace/case drift, cardinality, unmatched rows, and likely row multiplication. Use it to validate rows, schemas, duplicates, field quality, and delivery-readiness before handing data to clients or automations.

Categories: DEVELOPER_TOOLS

Keywords: csv, join, quality, checker, csv qa, data/schema qa utility

Pay-Per-Event Pricing

This actor uses pay-per-event pricing. Event prices include Apify platform usage; users are not expected to pay a separate platform-usage pass-through charge for the configured pricing model.

• Tier: U2 — Data/schema QA utility
• Primary event: row-checked at $0.00390 base
• Default max charge: $5.00
• Store discounts: FREE/BRONZE base, SILVER discounted, GOLD deepest approved discount

Event set:

• actor-start: base $0.00500, GOLD $0.00400. CSV Join Quality Checker: charged when actor start is completed. The price includes Apify platform usage; no separate usage pass-through is intended.
• row-checked: base $0.00390, GOLD $0.00312. CSV Join Quality Checker: charged when row checked is completed. The price includes Apify platform usage; no separate usage pass-through is intended.
• issue-detected: base $0.00372, GOLD $0.00298. CSV Join Quality Checker: charged when issue detected is completed. The price includes Apify platform usage; no separate usage pass-through is intended.
• qa-report-generated: base $0.05000, GOLD $0.04000. CSV Join Quality Checker: charged when qa report generated is completed. The price includes Apify platform usage; no separate usage pass-through is intended.

Public Task Concepts

• Audit CSV Join Quality controls on a capped public sample
• Find high-priority CSV Join Quality issues before release
• Validate CSV Join Quality evidence from supplied pages
• Prioritize CSV Join Quality fixes with severity and proof
• Export CSV Join Quality QA rows for client review

Computes CSV join readiness by normalized keys, including coverage, unmatched sides, duplicate counts, cardinality, and row multiplication risk without producing a merged dataset.

Inputs

• leftCsvUrl
• rightCsvUrl
• leftCsvText
• rightCsvText
• leftKeyColumns
• rightKeyColumns
• joinType
• normalizationRules
• caseSensitive
• trimWhitespace
• normalizeNumericKeys
• includeSampleRows
• maxRows
• timeoutMs
• maxChargeUsd

Public source provenance

The starter input uses https://raw.githubusercontent.com/datasets/country-list/master/data.csv joined to https://raw.githubusercontent.com/datasets/country-codes/master/data/country-codes.csv using ISO alpha-2 codes. The checked-in bounded snapshot keeps exact runs deterministic and avoids sending credentials or private data. Live URL inputs remain available when a current network check is required.

Outputs

Dataset fields: checkType, side, keyColumns, keyValue, normalizedKey, issueCode, severity, leftCount, rightCount, matchedCount, unmatchedCount, duplicateCount, cardinality, rowMultiplicationRisk, sampleRows, recommendation.

Reports: csv-join-quality-report.md, key-coverage-summary.csv, duplicate-keys.csv, unmatched-left-keys.csv, unmatched-right-keys.csv, join-cardinality-matrix.json.

Use small capped runs first; live network checks should stay bounded and avoid secrets in inputs.

Live and local execution use the same pay-per-event billing guard: the actor-start event is accepted before analysis, each paid row is charged before dataset output, reports are charged before key-value-store output, and maxChargeUsd stops gracefully without leaking unpaid artifacts.