Load TAK Server data to DuckDB

FreeTAKServer is a server platform that provides a REST API for managing users, geo-objects, emergencies, and missions. Everything needed to build a working TAK Server → 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 TAK Server 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 TAK Server 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 TAK Server 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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TAK Server API at a glance

These values come from the TAK Server API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the TAK Server API?

Requests require the Authorization header with the value 'Bearer '.

1. Get your credentials

TAK Server REST API authentication varies by distribution (e.g., CloudTAK vs. FreeTAKServer). For many production environments, you obtain an API key or bearer token via the administrative interface or by calling the API's authentication endpoint. \n\n1. Login via the Admin Web Dashboard (typically on port 8443) using your administrator certificate. Navigate to the API or user management section to generate or retrieve a long-lived API key/token. \n2. Alternatively, for programmatic access, perform a POST request to the login or token generation endpoint (e.g., /api/login or /api/token) using valid admin-level credentials or an existing admin bearer token to issue new scoped API keys. \n3. Ensure the token is stored securely (e.g., in a secrets manager or dlt's secrets.toml). For service-to-service communication, scoped API keys are preferred over user-session JWTs.

2. Add them to .dlt/secrets.toml

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[sources.tak_server_source]
api_key = "your_api_key_or_token_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 TAK Server data can I load into DuckDB?

These are the TAK Server endpoints dlt can load into DuckDB:

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How do I load only new TAK Server records?

The TAK Server 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": "missions", "endpoint": {
    "path": "Marti/api/missions",
    # 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 TAK Server pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /api/token and /Marti/api/ from the TAK Server API into DuckDB:

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

@dlt.source
def tak_server_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://<server_address>:19023/",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "missions", "endpoint": {"path": "Marti/api/missions", "data_selector": "data"}},
            {"name": "data_packages", "endpoint": {"path": "Marti/sync/search", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def load_tak_server_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="tak_server_pipeline",
        destination="duckdb",
        dataset_name="tak_server_data",
    )
    load_info = pipeline.run(tak_server_source())
    print(load_info)


if __name__ == "__main__":
    load_tak_server_to_duckdb()

Run it with python tak_server_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 TAK Server 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("tak_server_pipeline").dataset()
df = data.missions.df()
print(df.head())

SQL:

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SELECT * FROM tak_server_data.missions LIMIT 10;

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

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How do I deploy the TAK Server 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 TAK Server 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 TAK Server 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