Load Google Analytics data to Microsoft Fabric

Google Analytics Data API provides programmatic access to Google Analytics report data for properties and accounts. Everything needed to build a working Google Analytics → 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 Analytics 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 Analytics 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 Analytics API at a glance

These values come from the Google Analytics API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Google Analytics API?

Authentication is performed using OAuth 2.0. Requests require an Authorization header with a Bearer token, which can be generated via Application Default Credentials (e.g., using 'gcloud auth application-default print-access-token').

1. Get your credentials

1. Navigate to the Google Cloud Console (console.cloud.google.com) and select or create a project. 2. Enable the 'Google Analytics Data API' in the API Library. 3. Navigate to 'IAM & Admin' > 'Service Accounts'. 4. Click '+ Create Service Account', provide a name, and grant it the 'Viewer' role (or specific Analytics access if required). 5. Once created, click on the service account, go to the 'Keys' tab, select 'Add key' > 'Create new key', and choose 'JSON'. This will download a file containing your project_id, client_email, and private_key. 6. Grant this service account email ('client_email') access to your Google Analytics 4 property via the Google Analytics UI ('Admin' > 'Property Access Management').

2. Add them to .dlt/secrets.toml

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[sources.google_analytics_source]
project_id = "your_project_id"
client_email = "your_service_account_email@project.iam.gserviceaccount.com"
private_key = "-----BEGIN PRIVATE KEY-----\n...\n-----END PRIVATE KEY-----\n"

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 Analytics data can I load into Microsoft Fabric?

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

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How do I load only new Google Analytics records?

Google Analytics exposes page_token on v1beta/accountSummaries, 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": "account_summaries", "endpoint": {
    "path": "v1beta/accountSummaries",
    "data_selector": "accountSummaries",
    "incremental": {"cursor_path": "page_token", "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 Analytics pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading properties and runReport from the Google Analytics API into Microsoft Fabric:

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

@dlt.source
def google_analytics_source(credentials=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://analyticsdata.googleapis.com",
            "auth": {"type": "bearer", "token": credentials},
        },
        "resources": [
            {"name": "account_summaries", "endpoint": {"path": "v1beta/accountSummaries", "data_selector": "accountSummaries"}},
            {"name": "properties_list", "endpoint": {"path": "v1beta/properties", "data_selector": "properties"}}
        ],
    }
    yield from rest_api_resources(config)


def load_google_analytics_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="google_analytics_pipeline",
        destination="fabric",
        dataset_name="google_analytics_data",
    )
    load_info = pipeline.run(google_analytics_source())
    print(load_info)


if __name__ == "__main__":
    load_google_analytics_to_fabric()

Run it with uv run python google_analytics_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 Analytics 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_analytics_pipeline").dataset()
df = data.account_summaries.df()
print(df.head())

SQL:

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SELECT * FROM google_analytics_data.account_summaries LIMIT 10;

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

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How do I deploy the Google Analytics 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 Analytics 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 Analytics 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