Load MOVEit Automation data to DuckDB

MOVEit Automation REST API provides programmatic access to manage and monitor MOVEit Automation tasks and system configurations. Everything needed to build a working MOVEit Automation → 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 MOVEit Automation 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 MOVEit Automation 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 MOVEit Automation 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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MOVEit Automation API at a glance

These values come from the MOVEit Automation API documentation. Check them against the vendor's current reference before relying on them in production.

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How do I authenticate with the MOVEit Automation API?

Authentication requires requesting an access token via a POST request to /api/v1/token. Subsequent API requests must include the access token in the Authorization header using the Bearer scheme.

1. Get your credentials

To obtain access credentials for the MOVEit Automation REST API, you must request an authorization token using your MOVEit Automation server login details. Send a POST request to the /api/v1/token endpoint on your Web Admin server with a content type of application/x-www-form-urlencoded. The request body must include the grant_type set to password, your username, and your password. This request will return a JSON object containing an access_token and a refresh_token. The access token is then used in the Authorization header of subsequent API calls in the format Bearer <access_token>.

2. Add them to .dlt/secrets.toml

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[sources.moveit_automation_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 MOVEit Automation data can I load into DuckDB?

These are the MOVEit Automation endpoints dlt can load into DuckDB:

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How do I load only new MOVEit Automation records?

The MOVEit Automation 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": "tasks", "endpoint": {
    "path": "api/v1/tasks",
    # 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 MOVEit Automation pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /api/v1/token and /api/v1/tasks from the MOVEit Automation API into DuckDB:

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

@dlt.source
def moveit_automation_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<your-webadmin-server>/webadmin/api/v1",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "tasks", "endpoint": {"path": "api/v1/tasks", "data_selector": "items"}},
            {"name": "hosts", "endpoint": {"path": "api/v1/hosts", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


def load_moveit_automation_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="moveit_automation_pipeline",
        destination="duckdb",
        dataset_name="moveit_automation_data",
    )
    load_info = pipeline.run(moveit_automation_source())
    print(load_info)


if __name__ == "__main__":
    load_moveit_automation_to_duckdb()

Run it with python moveit_automation_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 MOVEit Automation 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("moveit_automation_pipeline").dataset()
df = data.tasks.df()
print(df.head())

SQL:

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SELECT * FROM moveit_automation_data.tasks LIMIT 10;

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

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How do I deploy the MOVEit Automation 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 MOVEit Automation 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 MOVEit Automation 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