Load Domino Data Lab data to DuckDB

Domino Data Lab provides a platform for managing data science projects, environments, and workloads, with a public REST API for core functionality like project and job management. Everything needed to build a working Domino Data Lab → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Domino Data Lab to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Domino Data Lab to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Domino Data Lab API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Domino Data Lab API at a glance

These values come from the Domino Data Lab API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Domino Data Lab API?

Authentication is performed by passing a bearer token in the 'Authorization' header (e.g., 'Authorization: Bearer ') for modern methods like Personal Access Tokens (PATs) and Service Account tokens. Legacy API keys are passed via the 'X-Domino-Api-Key' header.

1. Get your credentials

1. Log in to your Domino Data Lab account. 2. Click on your profile name in the menu. 3. Navigate to Account Settings. 4. Select API Key from the left-hand navigation menu. 5. Click Regenerate (or View/Copy) to obtain your key. Note: This key can only be viewed once upon generation. Keep it secure as it functions like a password. Note that Domino is transitioning away from API keys in favor of Service Accounts.

2. Add them to .dlt/secrets.toml

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[sources.domino_data_lab_source]
domino_api_key = "your_api_key_here"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Domino Data Lab data can I load into DuckDB?

These are the Domino Data Lab endpoints dlt can load into DuckDB:

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How do I load only new Domino Data Lab records?

The Domino Data Lab API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "projects", "endpoint": {
    "path": "api/projects/v1/projects",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Domino Data Lab pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /api/jobs/v1/jobs and /api/datasource/v1/datasources from the Domino Data Lab API into DuckDB:

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

@dlt.source
def domino_data_lab_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "The base URL is typically provided by the DOMINO_API_HOST environment variable within a Domino environment, or defaults to the host URL of your Domino deployment.",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "projects", "endpoint": {"path": "api/projects/v1/projects"}},
            {"name": "users", "endpoint": {"path": "api/users/v1/users"}}
        ],
    }
    yield from rest_api_resources(config)


def load_domino_data_lab_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="domino_data_lab_pipeline",
        destination="duckdb",
        dataset_name="domino_data_lab_data",
    )
    load_info = pipeline.run(domino_data_lab_source())
    print(load_info)


if __name__ == "__main__":
    load_domino_data_lab_to_duckdb()

Run it with python domino_data_lab_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Domino Data Lab data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("domino_data_lab_pipeline").dataset()
df = data.apps.df()
print(df.head())

SQL:

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SELECT * FROM domino_data_lab_data.apps LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Domino Data Lab to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Domino Data Lab loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Domino Data Lab data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack