Load Google Sheets data to Microsoft Fabric

Google Sheets API is a RESTful interface for reading and modifying Google Sheets spreadsheet data. Everything needed to build a working Google Sheets → Microsoft Fabric pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Google Sheets to Microsoft Fabric pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Google Sheets API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Google Sheets API at a glance

These values come from the Google Sheets API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Google Sheets API?

Requests require an 'Authorization: Bearer <access_token>' header. The access token is obtained via the OAuth 2.0 flow or service account credentials.

1. Get your credentials

To obtain credentials for the Google Sheets API, go to the Google Cloud Console and navigate to APIs & Services > Credentials. For most data pipelines (including dlt), a Service Account is recommended: 1. Click Create credentials > Service account. 2. Provide a name and description, then click Create and Continue. 3. Assign a role (usually 'Editor' or 'Viewer' for the project) and click Done. 4. In the Service accounts list, click on your new account, then go to the Keys tab. 5. Click Add key > Create new key, select JSON, and download the resulting file. This file contains the 'project_id', 'client_email', and 'private_key' needed for authentication. If you are using OAuth instead, create an OAuth 2.0 Client ID for a 'Web application' or 'Desktop app' and download the JSON file to extract 'client_id' and 'client_secret'.

2. Add them to .dlt/secrets.toml

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[sources.google_sheets_source]
credentials = "REPLACE_ME"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Google Sheets data can I load into Microsoft Fabric?

These are the Google Sheets endpoints dlt can load into Microsoft Fabric:

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How do I load only new Google Sheets records?

Google Sheets exposes N/A on spreadsheets/{spreadsheetId}/values/{range}, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "values", "endpoint": {
    "path": "spreadsheets/{spreadsheetId}/values/{range}",
    "data_selector": "values",
    "incremental": {"cursor_path": "N/A", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Google Sheets pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading spreadsheets.get and spreadsheets.values.get from the Google Sheets API into Microsoft Fabric:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def google_sheets_source(credentials=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://sheets.googleapis.com/v4/",
            "auth": {"type": "bearer", "token": credentials},
        },
        "resources": [
            {"name": "values", "endpoint": {"path": "spreadsheets/{spreadsheetId}/values/{range}", "data_selector": "values"}},
            {"name": "batch_get_values", "endpoint": {"path": "spreadsheets/{spreadsheetId}/values:batchGet", "data_selector": "valueRanges"}}
        ],
    }
    yield from rest_api_resources(config)


def load_google_sheets_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="google_sheets_pipeline",
        destination="fabric",
        dataset_name="google_sheets_data",
    )
    load_info = pipeline.run(google_sheets_source())
    print(load_info)


if __name__ == "__main__":
    load_google_sheets_to_fabric()

Run it with python google_sheets_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Google Sheets data in Microsoft Fabric?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("google_sheets_pipeline").dataset()
df = data.spreadsheets.df()
print(df.head())

SQL:

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SELECT * FROM google_sheets_data.spreadsheets LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Google Sheets to Microsoft Fabric pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Google Sheets loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Google Sheets data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack