# Dataset to Google Sheets — Reliable Export (`alaudinburki/dataset-to-google-sheets`) Actor

Append any Apify dataset to a Google Sheet via a service account, with a stable header, column union across ragged rows, and flattened nested values. Honest errors (e.g. share-the-sheet reminder). Pure logic.

- **URL**: https://apify.com/alaudinburki/dataset-to-google-sheets.md
- **Developed by:** [alaudin burki](https://apify.com/alaudinburki) (community)
- **Categories:** Integrations, Automation
- **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.00 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Dataset to Google Sheets — Reliable Export

Append any Apify dataset to a Google Sheet via a service account, with a stable header, column union across ragged rows, flattened nested values, and safe existing-header reconciliation. Honest errors (e.g. share-the-sheet reminder).

Built reliability-first: **every row reports what was found and what was missing** — you never get a
silent blank, and a run summary tells you exactly what happened.

### What you get

| Field | Description |
|---|---|
| `status` | Status |
| `spreadsheetId` | Spreadsheet Id |
| `sheet` | Sheet |
| `rowsWritten` | Rows Written |
| `columns` | Columns |
| `headerIncluded` | Header Included |
| `existingHeaderReused` | Existing columns were preserved rather than writing a duplicate header |
| `headerExtended` | Newly observed source fields were added to the existing header |
| `rowsUpdated` / `rowsAppended` | When `upsertKey` is set, matched rows updated in place and new rows appended |

### How to use it

1. Fill in the input (see the example below).
2. Run it once for a snapshot, or **schedule it** to keep the data fresh.
3. Export to CSV/JSON/Excel, or push straight to Google Sheets, Notion, Airtable, Zapier, Make, or n8n.

### Input

```json
{
  "serviceAccountJson": {},
  "spreadsheetId": "EX12345",
  "sheetName": "Sheet1",
  "items": [],
  "includeHeader": true
}
```

### Sample output

```json
[
{
  "status": "ok",
  "spreadsheetId": "EX12345",
  "sheet": "example",
  "rowsWritten": "example",
  "columns": "example",
  "headerIncluded": "example"
}
]
```

### Typical uses

- Schedule it and feed the output straight into your sheet, database, or app.
- Pair it with the related actors below to build a complete pipeline.

### Safe recurring exports

Leave **Reconcile existing header** on for a scheduled export. The Actor reads the first row of an
existing headed tab, keeps that column order, and extends it only when the source introduces a new
field. This prevents a changing scraper schema from shifting values under the wrong header. It also
retries an explicit Google `429` rate-limit response (never ambiguous write timeouts) and stores a
`WRITE_REPORT` with chunk progress if a later delivery chunk fails.

#### Optional idempotent exports

Set **Upsert key** to a stable source field such as `id` or `email` to update a matching existing
row instead of appending a duplicate. This requires a header row. The actor reads the existing tab
only in this mode, updates matching key rows through Google Sheets' batch API, and appends new or
empty keys. It refuses duplicate non-empty keys in either the input or existing Sheet rather than
guessing which record should win.

### Pricing

**$1.00 / 1,000 results** (`$0.001` per result), plus a near-zero start fee. You are **never charged beyond your limit**, and blocked or
failed items are reported honestly — not billed as data.

### FAQ & limitations

- Public data only — no login walls, no cookies required.
- Rate limits on the source may require the proxy or a retry on very large pulls.
- Every row reports its own status, so partial results are always labeled, never faked.
- **Integrations:** output works with Zapier, Make, n8n, and any webhook via Apify's integrations.
- **Formats:** results export as JSON, CSV, Excel, or HTML from the dataset.

### Related actors

- **Format Converter**
- **Dataset Profiler**
- **Dataset → Google Sheets**

# Actor input Schema

## `serviceAccountJson` (type: `object`):

Paste the full contents of your Google service-account JSON key. Then share the target spreadsheet with the service account's client\_email as an Editor. Stored as a secret.

## `spreadsheetId` (type: `string`):

The Google Sheet to append to — paste its URL or ID.

## `sheetName` (type: `string`):

The tab to append to (default Sheet1).

## `datasetId` (type: `string`):

The Apify dataset to export (e.g. a scraper run's defaultDatasetId).

## `items` (type: `array`):

Rows to export directly.

## `includeHeader` (type: `boolean`):

Write a header row of column names. Turn off when appending to a sheet that already has headers.

## `reconcileExistingHeader` (type: `boolean`):

When the tab already has a header row, preserve its column order and add newly seen fields to the end. Prevents changed source schemas from shifting values into the wrong columns.

## `upsertKey` (type: `string`):

A source field name such as id or email. Matching existing Sheet rows are updated; new keys are appended. The tab must have a header row and duplicate non-empty keys are rejected.

## `maxItems` (type: `integer`):

Cap on rows written. Leave empty for all.

## Actor input object example

```json
{
  "sheetName": "Sheet1",
  "items": [],
  "includeHeader": true,
  "reconcileExistingHeader": true
}
```

# Actor output Schema

## `results` (type: `string`):

The dataset of results produced by this run.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "sheetName": "Sheet1"
};

// Run the Actor and wait for it to finish
const run = await client.actor("alaudinburki/dataset-to-google-sheets").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "sheetName": "Sheet1" }

# Run the Actor and wait for it to finish
run = client.actor("alaudinburki/dataset-to-google-sheets").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "sheetName": "Sheet1"
}' |
apify call alaudinburki/dataset-to-google-sheets --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,alaudinburki/dataset-to-google-sheets"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/3LmTHEcZJRuz5lgqg/builds/D15PQAjHxmsEKcLtW/openapi.json