# Changes since the last run (snapshot mode)

**Use case:** 

Set a snapshot name and only supply the new data: the first run saves the baseline, every later run reports just the rows added, removed or changed since the previous run, then saves the new snapshot. Point 'New dataset' at your scraper's dataset (or chain it with an integration passing {{resource.defaultDatasetId}}) to get a delta feed without keeping dataset IDs.

## Input

```json
{
  "oldData": [
    {
      "sku": "A100",
      "title": "Blue Widget",
      "price": 19.99,
      "stock": 42
    },
    {
      "sku": "A101",
      "title": "Red Widget",
      "price": 24.99,
      "stock": 0
    },
    {
      "sku": "A102",
      "title": "Green Widget",
      "price": 15.5,
      "stock": 8
    }
  ],
  "newData": [
    {
      "sku": "A100",
      "title": "Blue Widget",
      "price": 17.99,
      "stock": 30
    },
    {
      "sku": "A102",
      "title": "Green Widget",
      "price": 15.5,
      "stock": 8
    },
    {
      "sku": "A103",
      "title": "Yellow Widget",
      "price": 12,
      "stock": 100
    }
  ],
  "fileFormat": "auto",
  "snapshotName": "example-shop-watch",
  "resetSnapshot": false,
  "keyFields": [
    "sku"
  ],
  "ignoreFields": [
    "scrapedAt",
    "lastChecked",
    "timestamp"
  ],
  "includeUnchanged": false,
  "maxItems": 0,
  "exportFormats": []
}
```

## Output

```json
{
  "status": {
    "label": "Status",
    "format": "text"
  },
  "key": {
    "label": "Key",
    "format": "object"
  },
  "changedFields": {
    "label": "Changed fields",
    "format": "array"
  },
  "oldValues": {
    "label": "Old values",
    "format": "object"
  },
  "newValues": {
    "label": "New values",
    "format": "object"
  }
}
```

## About this Actor

This example demonstrates how to use [Dataset Diff & Change Detector](https://apify.com/nerolabs/dataset-diff-detector.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/nerolabs/dataset-diff-detector.md) to learn more, explore other use cases, and run it yourself.


## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
This Task's input is already configured above — use it as-is rather than inventing a new one.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For full API examples (JavaScript, Python, CLI, MCP, OpenAPI), see this Task's Actor page: https://apify.com/nerolabs/dataset-diff-detector.md

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).