Load Contabo data to DuckDB

Contabo is a cloud infrastructure platform that provides a REST API for managing cloud resources like compute instances and snapshots. Everything needed to build a working Contabo → 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 Contabo 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 Contabo 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 Contabo 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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Contabo API at a glance

These values come from the Contabo API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Contabo API?

Requests require the 'Authorization: Bearer ' header and an 'x-request-id' header with a UUID4 value. Authentication is performed via OAuth2 by exchanging client credentials for an access token.

1. Get your credentials

1. Log in to the Contabo Customer Control Panel. 2. Navigate to the API section (often found under 'Account' > 'Security & Access' or simply 'API' on the sidebar). 3. View your 'Client ID' and 'Client Secret'. 4. To obtain an 'API Password', click the 'Send Link' button in the API section. This will email you a secure link to set your API password. You will need your Customer Control Panel login email (API User), API Password, Client ID, and Client Secret to authenticate.

2. Add them to .dlt/secrets.toml

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[sources.contabo_source]
access_token = "REPLACE_ME"

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 Contabo data can I load into DuckDB?

These are the Contabo endpoints dlt can load into DuckDB:

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How do I load only new Contabo records?

Contabo exposes page on v1/compute/instances, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "instances", "endpoint": {
    "path": "v1/compute/instances",
    "data_selector": "data",
    "incremental": {"cursor_path": "page", "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 Contabo pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /v1/compute/instances and /auth/realms/contabo/protocol/openid-connect/token from the Contabo API into DuckDB:

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

@dlt.source
def contabo_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.contabo.com/v1",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "instances", "endpoint": {"path": "v1/compute/instances", "data_selector": "data"}},
            {"name": "secrets", "endpoint": {"path": "v1/secrets", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_contabo_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="contabo_pipeline",
        destination="duckdb",
        dataset_name="contabo_data",
    )
    load_info = pipeline.run(contabo_source())
    print(load_info)


if __name__ == "__main__":
    load_contabo_to_duckdb()

Run it with python contabo_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 Contabo 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("contabo_pipeline").dataset()
df = data.instances.df()
print(df.head())

SQL:

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SELECT * FROM contabo_data.instances LIMIT 10;

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

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How do I deploy the Contabo 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 Contabo 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 Contabo 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