Load DataHub data to DuckDB

DataHub is an open-source metadata platform that provides REST and GraphQL APIs for managing and interacting with metadata entities. Everything needed to build a working DataHub → 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 DataHub 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 DataHub 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 DataHub 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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DataHub API at a glance

These values come from the DataHub API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the DataHub API?

DataHub API requests require an Authorization header with a Bearer token. The format is 'Authorization: Bearer ', where is typically a DataHub-issued Personal Access Token or an OAuth2/OIDC token.

1. Get your credentials

1. Log in to your DataHub instance.
2. Navigate to 'Settings' (gear icon in the top navigation).
3. Select 'Access Tokens' from the sidebar menu.
4. Click 'Generate new token'.
5. Provide a token name, choose the token type (Personal or Service Account), and set the desired expiration duration.
6. Click 'Generate' and save the resulting token string immediately in a secure location, as it will not be displayed again. Note: You must have the 'Generate Personal Access Tokens' privilege granted to your user account to see this option. If you need to generate a token for a service account, you will need the 'Manage All Access Tokens' privilege.

2. Add them to .dlt/secrets.toml

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[sources.datahub_source]
token = "your_generated_access_token_here"
# base_url is typically http://localhost:8080 or the URL of your GMS instance
base_url = "https://your-datahub-instance/gms"

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 DataHub data can I load into DuckDB?

These are the DataHub endpoints dlt can load into DuckDB:

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How do I load only new DataHub records?

The DataHub 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": "scroll_entities", "endpoint": {
    "path": "openapi/v3/entity/scroll",
    # 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 DataHub pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /entities/{urn} and /search from the DataHub API into DuckDB:

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

@dlt.source
def datahub_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://localhost:8080 (Metadata Service) or http://localhost:9002 (DataHub Frontend Proxy)",
            "auth": {"type": "bearer", "token": token},
        },
        "resources": [
            {"name": "scroll_entities", "endpoint": {"path": "openapi/v3/entity/scroll", "data_selector": "entities"}},
            {"name": "get_entity", "endpoint": {"path": "openapi/entities/v1/latest"}}
        ],
    }
    yield from rest_api_resources(config)


def load_datahub_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="datahub_pipeline",
        destination="duckdb",
        dataset_name="datahub_data",
    )
    load_info = pipeline.run(datahub_source())
    print(load_info)


if __name__ == "__main__":
    load_datahub_to_duckdb()

Run it with python datahub_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 DataHub 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("datahub_pipeline").dataset()
df = data.scroll_entities.df()
print(df.head())

SQL:

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SELECT * FROM datahub_data.scroll_entities LIMIT 10;

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

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How do I deploy the DataHub 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 DataHub 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 DataHub 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