Load Absorb lms data to DuckDB

Absorb Integration API is a set of RESTful endpoints used to manage and interact with data within the Absorb LMS environment. Everything needed to build a working Absorb lms → 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 Absorb lms 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 Absorb lms 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 Absorb lms 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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Absorb lms API at a glance

These values come from the Absorb lms API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Absorb lms API?

Requests to protected resources require an Authorization header with a Bearer token. Additionally, the X-API-Key header is required for all requests, carrying the private API key provided by the portal settings.

1. Get your credentials

To access the Absorb LMS Integration API, you must first obtain an API Private Key. This is typically provided by your Absorb account team or found within the portal settings. Log in to your Absorb LMS account, navigate to Admin > Portal Settings to view your credentials. To authenticate API requests, you must first generate an access token by making a POST request to the authentication endpoint, including your API Private Key, Username, and Password in the request body, while passing the Private Key in the 'x-api-key' request header.

2. Add them to .dlt/secrets.toml

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[sources.absorb_lms_source]
api_key = "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 Absorb lms data can I load into DuckDB?

These are the Absorb lms endpoints dlt can load into DuckDB:

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How do I load only new Absorb lms records?

The Absorb lms 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": "users", "endpoint": {
    "path": "users",
    # 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 Absorb lms pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /users and /users/upload from the Absorb lms API into DuckDB:

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

@dlt.source
def absorb_lms_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://rest.myabsorb.com",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "users"}},
            {"name": "courses", "endpoint": {"path": "courses"}}
        ],
    }
    yield from rest_api_resources(config)


def load_absorb_lms_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="absorb_lms_pipeline",
        destination="duckdb",
        dataset_name="absorb_lms_data",
    )
    load_info = pipeline.run(absorb_lms_source())
    print(load_info)


if __name__ == "__main__":
    load_absorb_lms_to_duckdb()

Run it with python absorb_lms_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 Absorb lms 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("absorb_lms_pipeline").dataset()
df = data.users.df()
print(df.head())

SQL:

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SELECT * FROM absorb_lms_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 Absorb lms 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 Absorb lms 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 Absorb lms 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