Dataset Diff & Change Intelligence Monitor

Compare two Apify datasets or inline snapshots and explain added, removed, and changed records plus field-level change frequencies.

Why use this Actor

Teams often know that two dataset snapshots differ but cannot quickly see which records or fields changed enough to trigger downstream action. 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

• two READ-scoped dataset pickers under Limited Permissions.
• record-level added/removed/changed classification.
• field-level change frequency summary.
• deterministic record hashing and fail-on-change threshold.
• 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 $0.0050. 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.