Firebolt Python API Docs | dltHub

Firebolt is a cloud data warehouse platform that provides a REST API and drivers to run SQL queries and manage engines programmatically. The REST API base URL is https://api.app.firebolt.io and all requests require a Bearer access token obtained with client credentials (service account).

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 Firebolt data in under 10 minutes.

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What data can I load from Firebolt?

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

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How do I authenticate with the Firebolt API?

Obtain an OAuth2 token via POST to https://id.app.firebolt.io/oauth/token with client_id and client_secret, then include the returned access_token in the Authorization: Bearer <access_token> header of every request.

1. Get your credentials

1. Open the Firebolt console and navigate to the Service Accounts section.
2. Click "Create Service Account" and ensure "property_is_organization_admin" is set to true.
3. Provide a name and assign the desired role.
4. After creation, record the Service Account ID (client_id) and Service Account Secret (client_secret).

2. Add them to .dlt/secrets.toml

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[sources.firebolt_data_warehouse_source]
access_token = "your_access_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 Firebolt 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 firebolt_data_warehouse_pipeline.py

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

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Pipeline firebolt_data_warehouse_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset firebolt_data_warehouse_data
The duckdb destination used duckdb:/firebolt_data_warehouse.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 firebolt_data_warehouse_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 system_engine_url and token from the Firebolt 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 firebolt_data_warehouse_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.app.firebolt.io",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "system_engine_url", "endpoint": {"path": "web/v3/account/{account_name}/engineUrl", "data_selector": "engineUrl"}},
            {"name": "information_schema_engines", "endpoint": {"path": "(run SQL: SELECT url FROM information_schema.engines WHERE engine_name='<engine_name>')", "data_selector": "(query result rows)"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="firebolt_data_warehouse_pipeline",
        destination="duckdb",
        dataset_name="firebolt_data_warehouse_data",
    )
    load_info = pipeline.run(firebolt_data_warehouse_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("firebolt_data_warehouse_pipeline").dataset()
sessions_df = data.system_engine_url.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM firebolt_data_warehouse_data.system_engine_url LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("firebolt_data_warehouse_pipeline").dataset()
data.system_engine_url.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 Firebolt 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 failures

If you receive 401 or 403 responses, verify that you exchanged the service account ID and secret at https://id.app.firebolt.io/oauth/token with grant_type=client_credentials and audience=https://api.firebolt.io. Ensure the returned access_token is sent as Authorization: Bearer <access_token> and that it has not expired.

Request size limits

User Engines: default request size limit is 2 MiB; System Engine: 32 KiB. Exceeding limits returns errors such as:

413: Request body is larger than configured limit of 20971520 bytes. Please contact support if you need to send larger queries to support your workload

Rate limits

System Engines are rate‑limited (see Firebolt docs); User Engines have no rate‑limits. When throttled, retry with backoff and respect any Retry-After headers.

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