Load Collibra data to DuckDB

Collibra is a data intelligence platform providing REST APIs for managing assets, domains, users, and governance workflows. Everything needed to build a working Collibra → 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 Collibra 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 Collibra 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 Collibra 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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Collibra API at a glance

These values come from the Collibra API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Collibra API?

Authentication is performed by including a JSON Web Token (JWT) as a Bearer token in the 'Authorization' HTTP header. Applications typically obtain this token from an Identity Provider (IdP) using the OAuth 2.0 client credentials flow.

1. Get your credentials

To obtain API credentials, navigate to the Collibra platform and go to Settings. Locate the Manage OAuth page (typically under Security or API Settings). Register a new 'Integration Application' to generate a Client ID and Client Secret, which are used for machine-to-machine authentication via the OAuth 2.0 client credentials flow.

2. Add them to .dlt/secrets.toml

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[sources.collibra_source]
client_id = "your_client_id_here"
client_secret = "your_client_secret_here"
base_url = "https://your-collibra-instance.com"

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 Collibra data can I load into DuckDB?

These are the Collibra endpoints dlt can load into DuckDB:

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How do I load only new Collibra records?

The Collibra 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": "assets", "endpoint": {
    "path": "assets",
    # 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 Collibra pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /rest/oauth/v2/token and /rest/2.0/... (various resource-specific endpoints) from the Collibra API into DuckDB:

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

@dlt.source
def collibra_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<your_collibra_url>/rest/2.0",
            "auth": {"type": "bearer", "token": token},
        },
        "resources": [
            {"name": "assets", "endpoint": {"path": "assets", "data_selector": "results"}},
            {"name": "domains", "endpoint": {"path": "domains", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def load_collibra_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="collibra_pipeline",
        destination="duckdb",
        dataset_name="collibra_data",
    )
    load_info = pipeline.run(collibra_source())
    print(load_info)


if __name__ == "__main__":
    load_collibra_to_duckdb()

Run it with python collibra_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 Collibra 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("collibra_pipeline").dataset()
df = data.assets.df()
print(df.head())

SQL:

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SELECT * FROM collibra_data.assets LIMIT 10;

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

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How do I deploy the Collibra 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 Collibra 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 Collibra 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