Dataset Deduplication & Merge Planner

Find duplicate groups in Apify datasets, quantify duplicate risk, and generate deterministic merge recommendations without mutating the source. Gen2 adds baseline comparison, economic impact, confidence, and explicit automation decisions. Returns structured, machine-readable results.

Quick start

Start with this working example and replace the target values with your own:

{
  "items": [
    {
      "id": 1,
      "name": "A"
    },
    {
      "id": 1,
      "name": "A2"
    },
    {
      "id": 2,
      "name": "B"
    }
  ],
  "keyFields": [
    "id"
  ]
}

The Actor writes structured results to the default Apify Dataset and can be used from the Store, API, schedules, Tasks, automations and MCP-compatible AI workflows.

Input

• datasetId — Dataset: Optional Apify dataset selected with least-privilege READ access.
• items — Inline items: Optional inline JSON objects for tests or small manual runs.
• maxItems — Maximum items: Maximum dataset rows to evaluate.
• keyFields — Key fields: Composite fields that define duplicate identity.
• mergeStrategy — Merge strategy: first, last, or non_null.
• costPerDuplicateUsd — Cost per duplicate (USD): Optional estimated cost of carrying or remediating one excess duplicate.

All integration and advanced analysis fields are optional. The default example is intentionally runnable without configuring MCP or a previous-run baseline.

Output

The Dataset exposes predictable machine-readable output. Representative fields include results.

Pricing

This Actor uses Pay Per Event. The current factory base price is $0.004000 per result event. The Apify Store remains the source of truth for the price and plan/tier details shown to the buyer.

Use cases

• Run the buyer-ready workflows exposed as Apify Tasks without preparing a custom integration first.
• Use the Actor from API or schedules for recurring collection, comparison or monitoring.
• Feed the structured Dataset output into spreadsheets, databases, automations or AI agents.
• Compare repeat runs when the product supports snapshots or previous-run inputs.

Automation and AI

Use the same Actor through Apify API, schedules, public Tasks and the Apify MCP server. Outputs are structured for downstream workflows and AI agents rather than requiring manual copy/paste.

Limitations

• Public websites and upstream APIs can change markup, access rules, rate limits or field availability without notice.
• Fields that are not publicly available are returned as unavailable or omitted rather than fabricated.
• Analytical outputs depend on the quality and coverage of the supplied or collected source data.
• Treat marketplace, reputation, workforce, safety or commercial signals as decision support and validate material decisions against the underlying source evidence.

Detailed documentation

Detailed documentation

Find duplicate groups in Apify datasets, quantify duplicate risk, and generate deterministic merge recommendations without mutating the source.

Why use this Actor

Duplicate rows propagate into CRMs, warehouses, lead lists, and AI datasets, while automatic deletion can be too risky without explainable grouping. This Actor is designed for repeatable automation rather than a one-off demo. It produces structured, machine-readable evidence that can be scheduled, called through the API, saved as an Apify Task, or inserted into a larger Actor-to-Actor workflow. The goal is to make the result useful to both humans and automated agents without hiding the decision behind an opaque score.

Key features

• multi-field composite keys.
• duplicate group evidence with masked samples.
• first/last/non-null merge recommendation.
• READ-only source safety with no destructive mutation.
• Structured output designed for downstream automation and monitoring.
• Deterministic behavior suitable for regression tests and scheduled Tasks.
• No external paid AI service is required for the core result.

Input

Use the Actor input form or API. Dataset-oriented products accept inline test data and, where applicable, an Apify dataset selected through a READ-scoped resource picker. Monitor-oriented products accept a current state plus a previous baseline. The input schema documents every field so Apify AI and other agents can select and populate the Actor without guessing what a parameter means.

Output

The default dataset contains a structured report with explicit status fields, counts, fingerprints, changes, and issue codes appropriate to this product. Fingerprints are deterministic for the normalized input state, which makes them useful for recurring comparisons. The source data is not silently mutated. When a fail-run option is enabled, the Actor writes the diagnostic report first and then fails the run so an upstream scheduler or integration can stop downstream processing.

Example

Create a Task using the provided default input, replace the example values with your own dataset or baseline, run it once to establish evidence, and save the resulting fingerprint or report as the next comparison baseline. For scheduled workflows, run the same Task hourly, daily, or weekly according to how quickly the underlying data can change.

Use cases

• CRM ingestion workflows.
• RAG ingestion workflows.
• e-commerce catalog workflows.
• job feed workflows.
• lead enrichment workflows.
• real-estate feed workflows.
• analytics warehouse workflows.
• financial feed workflows.
• review aggregation workflows.
• social metrics workflows.

Pricing

The planned pricing is pay-per-event with one clear primary event for one completed structured report. The current planned event price is $0.0040. The design intentionally avoids artificial premium events. If future versions add an independently valuable enrichment or action, pricing will be reviewed against real usage and platform costs before any change.

Limitations

This Actor evaluates the configured input and does not promise semantic truth beyond observable data. Very large datasets are capped by the configured maximum-item limit. Nested object semantics are intentionally conservative unless the product explicitly reports them. Baselines supplied by the user remain the user's responsibility. A PASS means the configured rules passed, not that every possible business or compliance requirement has been satisfied.

Integration and automation

Use Apify Tasks for reusable presets and Schedules for recurring checks. Dataset-oriented variants are designed around least-privilege access so a selected dataset can be read without granting unrestricted access to unrelated storage. The structured output can be consumed by another Actor, webhook receiver, database loader, alerting layer, CI system, or AI agent.

Commercial intelligence layer

Gen2 preserves duplicate grouping and merge previews while adding baseline duplicate-rate comparison, excess-duplicate counts, key-completeness confidence, configurable risk thresholds, optional cost-per-duplicate economics, annualized savings, and a GO / WARN / BLOCK decision. Source datasets remain read-only.