Fauna Python API Docs | dltHub

Fauna is a transactional serverless cloud database with FQL and hosted GraphQL that exposes HTTP endpoints for queries and data operations. The REST API base URL is https://db.fauna.com and All requests require a Fauna secret used as a Bearer token..

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 Fauna data in under 10 minutes.

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What data can I load from Fauna?

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

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How do I authenticate with the Fauna API?

Obtain a Fauna secret (key) from the dashboard and include it in requests as an HTTP header: Authorization: Bearer <FAUNA_SECRET>. Driver libraries (faunadb) accept the secret when creating FaunaClient(secret=...).

1. Get your credentials

1. Open https://dashboard.fauna.com and log in. 2) Select the database (or create one). 3) Open Security (or Keys) and click 'New Key'. 4) Choose a role (Admin, Server, Client) and create the key. 5) Copy the generated secret and store it securely (do not commit).

2. Add them to .dlt/secrets.toml

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[sources.fauna_instance_source]
fauna_secret = "your_fauna_secret_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 Fauna 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 fauna_instance_pipeline.py

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

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Pipeline fauna_instance_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset fauna_instance_data
The duckdb destination used duckdb:/fauna_instance.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 fauna_instance_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 documents and graphql from the Fauna 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 fauna_instance_source(fauna_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://db.fauna.com",
            "auth": {
                "type": "bearer",
                "token": fauna_secret,
            },
        },
        "resources": [
            {"name": "documents", "endpoint": {"path": "(FQL POST to) db.fauna.com/", "data_selector": "data"}},
            {"name": "graphql", "endpoint": {"path": "graphql.fauna.com/graphql", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="fauna_instance_pipeline",
        destination="duckdb",
        dataset_name="fauna_instance_data",
    )
    load_info = pipeline.run(fauna_instance_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("fauna_instance_pipeline").dataset()
sessions_df = data.documents.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM fauna_instance_data.documents LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("fauna_instance_pipeline").dataset()
data.documents.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 Fauna 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

Ensure the Authorization header is set to "Authorization: Bearer <FAUNA_SECRET>". Missing or invalid secrets produce Unauthorized / PermissionDenied errors. Create a Server or Admin key for server-side operations; use client-scoped keys for end-user operations.

Permission and role errors

If a secret lacks privileges for an action, Fauna returns permission-denied/403. Use roles and ABAC correctly; create keys with appropriate roles (Server/Admin) for full read/write operations.

Pagination and result shape

Paginated FQL queries (q.paginate(...)) return an object with a top-level "data" key containing the list of items. Drivers and examples access result['data'] to get records.

Bad requests and malformed FQL

Malformed FQL queries or invalid GraphQL queries return BadRequest errors; validate queries and test with Fauna drivers or GraphQL playground.

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