Load GenieACS data to DuckDB

GenieACS provides a Northbound Interface (NBI) that exposes a REST API for managing devices and configuration parameters. Everything needed to build a working GenieACS → 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 GenieACS 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 GenieACS 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 GenieACS 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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GenieACS API at a glance

These values come from the GenieACS API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the GenieACS API?

Authentication is optional and enabled by setting the NBI_AUTHENTICATION_KEY environment variable. When configured, all requests require the 'x-api-key' HTTP header containing the configured secret key.

1. Get your credentials

The GenieACS Northbound Interface (NBI) does not have native authentication enabled by default. To secure the API, you must configure the NBI_AUTHENTICATION_KEY environment variable in your genieacs.env configuration file. You can generate a secure key using a command such as openssl rand -hex 32. Once set, restart the genieacs-nbi service for the configuration to take effect. All subsequent requests to the NBI API must include the x-api-key header with this value.

2. Add them to .dlt/secrets.toml

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[sources.genieacs_source]
api_key = "your_generated_secret_key_here"

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 GenieACS data can I load into DuckDB?

These are the GenieACS endpoints dlt can load into DuckDB:

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How do I load only new GenieACS records?

GenieACS exposes _id on devices/, 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": "devices", "endpoint": {
    "path": "devices/",
    "incremental": {"cursor_path": "_id", "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 GenieACS pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /devices and /tasks from the GenieACS API into DuckDB:

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

@dlt.source
def genieacs_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://localhost:7557",
            "auth": {"type": "api_key", "api_key": api_key, "name": "x-api-key", "location": "header"},
        },
        "resources": [
            {"name": "devices", "endpoint": {"path": "devices/"}},
            {"name": "tasks", "endpoint": {"path": "tasks/"}}
        ],
    }
    yield from rest_api_resources(config)


def load_genieacs_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="genieacs_pipeline",
        destination="duckdb",
        dataset_name="genieacs_data",
    )
    load_info = pipeline.run(genieacs_source())
    print(load_info)


if __name__ == "__main__":
    load_genieacs_to_duckdb()

Run it with python genieacs_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 GenieACS 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("genieacs_pipeline").dataset()
df = data.devices.df()
print(df.head())

SQL:

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SELECT * FROM genieacs_data.devices LIMIT 10;

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

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How do I deploy the GenieACS 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 GenieACS 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 GenieACS 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