Nuvi Python API Docs | dltHub

Garmin Nuvi is a navigation platform that provides routing and map services for Garmin devices. The REST API base URL is `` and Authentication is performed with an API key obtained from the Garmin Connect Developer Program..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Nuvi data in under 10 minutes.

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What data can I load from Nuvi?

Here are some of the endpoints you can load from Nuvi:

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How do I authenticate with the Nuvi API?

Include the API key in the request header, e.g., api-key: <your_key>.

1. Get your credentials

1. Sign up for the Garmin Connect Developer Program at https://developer.garmin.com/gc-developer-program/.\n2. Log in to the developer portal https://developerportal.garmin.com/developer-programs/connect-developer-api.\n3. Create a new application in the portal.\n4. Submit the request for API keys for your application.\n5. Once approved, view and copy the API key from the application details page.

2. Add them to .dlt/secrets.toml

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[sources.nuvi_source]
api_key = "your_api_key_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Nuvi API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python nuvi_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline nuvi_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset nuvi_data
The duckdb destination used duckdb:/nuvi.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline nuvi_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads activities and health from the Nuvi API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def nuvi_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "activities", "endpoint": {"path": "activity", "data_selector": "activities"}},
            {"name": "health", "endpoint": {"path": "health", "data_selector": "healthData"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="nuvi_pipeline",
        destination="duckdb",
        dataset_name="nuvi_data",
    )
    load_info = pipeline.run(nuvi_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("nuvi_pipeline").dataset()
sessions_df = data.activities.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM nuvi_data.activities LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("nuvi_pipeline").dataset()
data.activities.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Nuvi data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication Errors

• 401 Unauthorized: Occurs when the API key is missing, invalid, or not approved. Verify that your API key is correct and that your developer account has been granted access.
• 403 Forbidden: The API key may lack required permissions for the requested resource.

Rate Limiting

• Garmin APIs enforce rate limits per application. Exceeding the limit returns 429 Too Many Requests. Implement exponential back‑off and respect the Retry-After header.

Endpoint Changes / Unofficial API

• As noted by community members, unofficial endpoints may change without notice. If you rely on reverse‑engineered URLs, you may encounter 404 Not Found or unexpected response structures when Garmin updates their services. Prefer using the official endpoints provided after program approval.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack