10000ft Python API Docs | dltHub

10000ft API is an Application Programming Interface that allows users to retrieve and add data from its databases. The REST API base URL is https://api.rm.smartsheet.com/api/v1 and All requests require a service API token for authentication..

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 10000ft data in under 10 minutes.

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What data can I load from 10000ft?

Here are some of the endpoints you can load from 10000ft:

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How do I authenticate with the 10000ft API?

Authentication uses a service API token that can be sent either in an HTTP header named "auth" or as a query parameter "auth". Sending it in the header is recommended.

1. Get your credentials

Account administrators can generate API tokens in the app under Settings > Developer API. API Token Management endpoints also exist under /api/api_tokens.

2. Add them to .dlt/secrets.toml

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[sources._10000ft_source]
auth = "your_api_token_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 10000ft 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 _10000ft_pipeline.py

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

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Pipeline _10000ft_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset _10000ft_data
The duckdb destination used duckdb:/_10000ft.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 _10000ft_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 users and projects from the 10000ft 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 _10000ft_source(auth=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.rm.smartsheet.com/api/v1",
            "auth": {
                "type": "api_key",
                "auth": auth,
            },
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "users", "data_selector": "data"}},
            {"name": "projects", "endpoint": {"path": "projects", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="_10000ft_pipeline",
        destination="duckdb",
        dataset_name="_10000ft_data",
    )
    load_info = pipeline.run(_10000ft_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("_10000ft_pipeline").dataset()
sessions_df = data.users.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM _10000ft_data.users LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("_10000ft_pipeline").dataset()
data.users.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 10000ft 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

Pagination

Collection responses are paginated and include a paging object. You can control pagination using per_page and page query parameters. The per_page parameter has a default of 20 and a maximum of 1000 rows. The paging object also provides next and previous URLs for navigation.

Rate Limits

When the API rate limit is exceeded, a standard HTTP 429 Too Many Requests response will be returned. Rate limit information is provided in X-RateLimit-* headers.

Error Handling

The API uses standard HTTP 4xx/5xx status codes. Specific error responses include:

• 400 Bad Request: Returned for invalid JSON in the request body, often with a {"message":...} JSON object.
• 401 Unauthorized or 403 Forbidden: Indicates authentication or authorization issues.
• 404 Not Found: Returned when the requested resource does not exist.
• 429 Too Many Requests: Indicates that the rate limit has been exceeded.

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