Sigma Computing Python API Docs | dltHub

Sigma Computing is a cloud‑based analytics platform that provides a REST API for programmatic access to analytics resources. The REST API base URL is https://api.sigmacomputing.com and All requests require a Bearer token obtained from the /v2/auth/token endpoint..

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 Sigma Computing data in under 10 minutes.

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What data can I load from Sigma Computing?

Here are some of the endpoints you can load from Sigma Computing:

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How do I authenticate with the Sigma Computing API?

Obtain a bearer token by POSTing to /v2/auth/token with your client ID and client secret, then include it in the HTTP header as "Authorization: Bearer ".

1. Get your credentials

1. Log in to the Sigma Computing web UI as an organization admin.\n2. Navigate to Settings → API Credentials (or similar).\n3. Click Create New Credential and select OAuth client credentials.
2. Record the generated Client ID and Client Secret.\n5. Use the client ID and secret to call the /v2/auth/token endpoint and obtain a bearer token (valid for 1 hour).

2. Add them to .dlt/secrets.toml

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[sources.sigma_computing_source]
token = "your_bearer_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 Sigma Computing 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 sigma_computing_pipeline.py

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

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Pipeline sigma_computing_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset sigma_computing_data
The duckdb destination used duckdb:/sigma_computing.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 sigma_computing_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 members and workbooks from the Sigma Computing 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 sigma_computing_source(client_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.sigmacomputing.com",
            "auth": {
                "type": "bearer",
                "token": client_id,
            },
        },
        "resources": [
            {"name": "members", "endpoint": {"path": "v2/members"}},
            {"name": "workbooks", "endpoint": {"path": "v2/workbooks"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="sigma_computing_pipeline",
        destination="duckdb",
        dataset_name="sigma_computing_data",
    )
    load_info = pipeline.run(sigma_computing_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("sigma_computing_pipeline").dataset()
sessions_df = data.members.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM sigma_computing_data.members LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("sigma_computing_pipeline").dataset()
data.members.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 Sigma Computing 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 bearer token is missing, malformed, or expired. Tokens issued by /v2/auth/token expire after 1 hour. Refresh the token using your client ID and secret.

Rate Limiting

• The /v2/auth/token endpoint is limited to 1 request per second. Exceeding this limit returns a 429 Too Many Requests response. Implement back‑off or token caching to stay within limits.

Pagination Limits

• List endpoints default to a page size of 50 and allow a maximum of 1000 records per request. Use the page and pageSize query parameters (if available) to paginate through larger result sets.

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