Load Google My Business data to BigQuery

Google Business Profile APIs allow developers to programmatically manage business location information and data on Google. Everything needed to build a working Google My Business → BigQuery 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 My Business to BigQuery 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 My Business 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 My Business API at a glance

These values come from the Google My Business API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Google My Business API?

Every request requires an OAuth 2.0 access token passed as an 'Authorization' header in the format 'Bearer {access_token}'.

1. Get your credentials

1. Access the Google Cloud Console and select or create a project. 2. Navigate to 'APIs & Services' > 'Enabled APIs & services' to enable the necessary Business Profile APIs. 3. Navigate to 'APIs & Services' > 'OAuth consent screen' to configure your app's name, logo, and scopes (e.g., https://www.googleapis.com/auth/business.manage). 4. Navigate to 'APIs & Services' > 'Credentials'. 5. Click 'Create Credentials' and select 'OAuth client ID'. 6. Choose 'Web application' as the application type, provide authorized redirect URIs, and click 'Create' to obtain your Client ID and Client Secret. 7. If required for your application, ensure you have submitted the API access request form via the Google Business Profile API dashboard.

2. Add them to .dlt/secrets.toml

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[sources.google_my_business_source]
client_id = "YOUR_CLIENT_ID"
client_secret = "YOUR_CLIENT_SECRET"
refresh_token = "YOUR_REFRESH_TOKEN"

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 My Business data can I load into BigQuery?

These are the Google My Business endpoints dlt can load into BigQuery:

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How do I load only new Google My Business records?

Google My Business exposes pageToken on v1/accounts, 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": "accounts", "endpoint": {
    "path": "v1/accounts",
    "data_selector": "accounts",
    "incremental": {"cursor_path": "pageToken", "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 My Business pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading https://mybusiness.googleapis.com and https://businessprofileperformance.googleapis.com/v1/ from the Google My Business API into BigQuery:

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

@dlt.source
def google_my_business_source(credentials=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://mybusiness.googleapis.com",
            "auth": {"type": "bearer", "token": credentials},
        },
        "resources": [
            {"name": "accounts", "endpoint": {"path": "v1/accounts", "data_selector": "accounts"}},
            {"name": "accounts_locations", "endpoint": {"path": "v1/{parent=accounts/*}/locations", "data_selector": "locations"}}
        ],
    }
    yield from rest_api_resources(config)


def load_google_my_business_to_bigquery() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="google_my_business_pipeline",
        destination="bigquery",
        dataset_name="google_my_business_data",
    )
    load_info = pipeline.run(google_my_business_source())
    print(load_info)


if __name__ == "__main__":
    load_google_my_business_to_bigquery()

Run it with uv run python google_my_business_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 My Business data in BigQuery?

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_my_business_pipeline").dataset()
df = data.accounts.df()
print(df.head())

SQL:

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SELECT * FROM google_my_business_data.accounts LIMIT 10;

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

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How do I deploy the Google My Business to BigQuery 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 My Business 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 My Business 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