Load Liquid Instruments Moku data to DuckDB

Liquid Instruments Moku API provides a RESTful interface for command, control, and monitoring of Moku test and measurement devices. Everything needed to build a working Liquid Instruments Moku → 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 Liquid Instruments Moku 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 Liquid Instruments Moku 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 Liquid Instruments Moku 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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Liquid Instruments Moku API at a glance

These values come from the Liquid Instruments Moku API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Liquid Instruments Moku API?

Authentication requires obtaining a Moku-Client-Key by POSTing to the /api/moku/claim_ownership endpoint, which is then provided in the Moku-Client-Key header for all subsequent requests.

1. Get your credentials

To authenticate with a Moku device via the REST API, you must 'claim ownership' of the device. This is done by sending an HTTP POST request to the /api/moku/claim_ownership endpoint with an empty JSON body. The device will respond with a Moku-Client-Key in the HTTP response headers. You must record this key and include it in the Moku-Client-Key header for all subsequent REST API requests to that specific device. Note that client libraries for Python or MATLAB typically handle this process implicitly.

2. Add them to .dlt/secrets.toml

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[sources.liquid_instruments_moku_source]
moku_client_key = "your_moku_client_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 Liquid Instruments Moku data can I load into DuckDB?

These are the Liquid Instruments Moku endpoints dlt can load into DuckDB:

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How do I load only new Liquid Instruments Moku records?

The Liquid Instruments Moku 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": "moku_ownership", "endpoint": {
    "path": "moku/claim_ownership",
    # 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 Liquid Instruments Moku pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /api/moku/claim_ownership and /api/moku/relinquish_ownership from the Liquid Instruments Moku API into DuckDB:

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

@dlt.source
def liquid_instruments_moku_source(moku_client_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://<ip>/api",
            "auth": {"type": "api_key", "api_key": moku_client_key, "name": "Moku-Client-Key", "location": "header"},
        },
        "resources": [
            {"name": "moku_ownership", "endpoint": {"path": "moku/claim_ownership"}},
            {"name": "oscilloscope_deploy", "endpoint": {"path": "oscilloscope/deploy"}}
        ],
    }
    yield from rest_api_resources(config)


def load_liquid_instruments_moku_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="liquid_instruments_moku_pipeline",
        destination="duckdb",
        dataset_name="liquid_instruments_moku_data",
    )
    load_info = pipeline.run(liquid_instruments_moku_source())
    print(load_info)


if __name__ == "__main__":
    load_liquid_instruments_moku_to_duckdb()

Run it with python liquid_instruments_moku_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 Liquid Instruments Moku 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("liquid_instruments_moku_pipeline").dataset()
df = data.moku_ownership.df()
print(df.head())

SQL:

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SELECT * FROM liquid_instruments_moku_data.moku_ownership LIMIT 10;

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

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How do I deploy the Liquid Instruments Moku 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 Liquid Instruments Moku 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 Liquid Instruments Moku 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