Load PlexTrac data to DuckDB

PlexTrac is a cybersecurity platform for managing pentesting assessments, findings, and reports which provides a REST API for automated data integration. Everything needed to build a working PlexTrac → 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 PlexTrac 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 PlexTrac 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 PlexTrac 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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PlexTrac API at a glance

These values come from the PlexTrac 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 PlexTrac API?

All requests require a JWT Bearer token included in the Authorization header as 'Authorization: Bearer '.

1. Get your credentials

PlexTrac typically uses JWT-based authentication for its REST API, which involves exchanging user credentials for a temporary bearer token. To obtain credentials: 1. Navigate to your PlexTrac instance. 2. For standard programmatic access, most users generate an API key via the dashboard by navigating to User Settings > API Keys and selecting Generate New API Key. 3. If using the authentication endpoint directly, perform a POST request to /api/v1/authenticate (or /api/v2/authentication) with your username and password to receive a token in the response body. If MFA is enabled, you must provide the additional MFA code in the authentication request. Note that tokens are typically short-lived (e.g., 15 minutes).

2. Add them to .dlt/secrets.toml

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[sources.plextrac_source]
plextrac_api_key = "your_api_key_or_token_here"
plextrac_base_url = "https://your_instance_url/api/v2"

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 PlexTrac data can I load into DuckDB?

These are the PlexTrac endpoints dlt can load into DuckDB:

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How do I load only new PlexTrac records?

The PlexTrac 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": "findings", "endpoint": {
    "path": "api/v2/findings",
    # 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 PlexTrac pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading authentication and clients from the PlexTrac API into DuckDB:

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

@dlt.source
def plextrac_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<your_plextrac_instance>/api/v2",
            "auth": {"type": "bearer", "token": token},
        },
        "resources": [
            {"name": "findings", "endpoint": {"path": "api/v2/findings"}},
            {"name": "clients", "endpoint": {"path": "api/v2/clients"}}
        ],
    }
    yield from rest_api_resources(config)


def load_plextrac_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="plextrac_pipeline",
        destination="duckdb",
        dataset_name="plextrac_data",
    )
    load_info = pipeline.run(plextrac_source())
    print(load_info)


if __name__ == "__main__":
    load_plextrac_to_duckdb()

Run it with python plextrac_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 PlexTrac 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("plextrac_pipeline").dataset()
df = data.findings.df()
print(df.head())

SQL:

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SELECT * FROM plextrac_data.findings LIMIT 10;

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

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How do I deploy the PlexTrac 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 PlexTrac 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 PlexTrac 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