Load Docebo data to DuckDB

Docebo is a learning management system providing a REST API for managing users, courses, enrollments, and other platform data. Everything needed to build a working Docebo → 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 Docebo 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 Docebo 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 Docebo 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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Docebo API at a glance

These values come from the Docebo API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Docebo API?

Docebo uses OAuth 2.0 authentication. Requests must include the access token in the Authorization header using the format 'Bearer '.

1. Get your credentials

1. Log in to your Docebo platform as a Superadmin. 2. Click the gear icon in the top right corner to open the Admin menu. 3. Navigate to the API and SSO section and click Manage. 4. Select the API Credentials vertical tab. 5. Click the Add OAuth2 App button. 6. Provide an App Name, App Description, and Client ID (a unique name for your application). 7. The system will automatically generate a Client Secret; copy and store this securely. 8. Enter the required Redirect URL (if required by your integration, e.g., for OAuth flows). 9. Configure grant types under advanced settings as needed (e.g., Authorization code + implicit grant, Client credentials). 10. Click Confirm and then ensure the app is activated (the icon next to the app name will turn green).

2. Add them to .dlt/secrets.toml

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[sources.docebo_source]
client_id = "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 Docebo data can I load into DuckDB?

These are the Docebo endpoints dlt can load into DuckDB:

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How do I load only new Docebo records?

Docebo exposes LAST_UPDATE on manage/v1/user, 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": "users", "endpoint": {
    "path": "manage/v1/user",
    "data_selector": "data",
    "incremental": {"cursor_path": "LAST_UPDATE", "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 Docebo pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /manage/v1/user and /learn/v1/catalog/filters from the Docebo API into DuckDB:

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

@dlt.source
def docebo_source(client_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{subdomain}.docebosaas.com",
            "auth": {"type": "bearer", "token": client_id},
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "manage/v1/user", "data_selector": "data"}},
            {"name": "courses", "endpoint": {"path": "learn/v1/courses", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_docebo_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="docebo_pipeline",
        destination="duckdb",
        dataset_name="docebo_data",
    )
    load_info = pipeline.run(docebo_source())
    print(load_info)


if __name__ == "__main__":
    load_docebo_to_duckdb()

Run it with python docebo_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 Docebo 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("docebo_pipeline").dataset()
df = data.users.df()
print(df.head())

SQL:

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SELECT * FROM docebo_data.users LIMIT 10;

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

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How do I deploy the Docebo 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 Docebo 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 Docebo 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