Cooperata Python API Docs | dltHub

Cooperata is Global Fishing Watch’s REST API portal providing vessel identity, events, insights, 4Wings map/reporting and bulk‑download endpoints for ocean and vessel data. The REST API base URL is https://gateway.api.globalfishingwatch.org/v3 and All requests require a Bearer token provided in the Authorization header..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Cooperata data in under 10 minutes.

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What data can I load from Cooperata?

Here are some of the endpoints you can load from Cooperata:

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How do I authenticate with the Cooperata API?

The API uses HTTP Bearer token authentication. Include header: Authorization: Bearer <YOUR_TOKEN>. Some endpoints also require Content-Type: application/json and Accept: application/json.

1. Get your credentials

1. Register or sign in to a Global Fishing Watch account: https://globalfishingwatch.org/our-apis/tokens/signup
2. Open the API Access Tokens page: https://globalfishingwatch.org/our-apis/tokens
3. Create a new API access token and copy the generated token.
4. Store this token securely (see secrets_toml_example below).

2. Add them to .dlt/secrets.toml

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[sources.cooperata_source]
api_key = "your_api_token_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Cooperata API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python cooperata_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline cooperata_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset cooperata_data
The duckdb destination used duckdb:/cooperata.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline cooperata_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads vessels and events from the Cooperata API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def cooperata_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://gateway.api.globalfishingwatch.org/v3",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "vessels", "endpoint": {"path": "v3/vessels"}},
            {"name": "events", "endpoint": {"path": "v3/events"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="cooperata_pipeline",
        destination="duckdb",
        dataset_name="cooperata_data",
    )
    load_info = pipeline.run(cooperata_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("cooperata_pipeline").dataset()
sessions_df = data.events.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM cooperata_data.events LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("cooperata_pipeline").dataset()
data.events.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Cooperata data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If you receive 401 Unauthorized or 403 Forbidden, verify your Authorization header is exactly "Authorization: Bearer " and that the token is active. Regenerate a token from the API Access Tokens page if needed.

Rate limits and Terms of Use

Requests are limited by the API Terms of Use — ensure you include your API token for each request and avoid high‑frequency polling. The docs show example 429 responses for rate‑limited endpoints (e.g., 4Wings reporting). Cache GET requests where possible.

Pagination and offsets

List endpoints use limit and offset query parameters. Example list responses include top‑level fields: total, limit, offset, nextOffset and a paginated array in entries for some endpoints. Use offset += limit to page through results.

Report jobs and bulk downloads

Bulk report creation returns a report object with status (pending/done) and, when done, a signed URL in a url field for downloading the report. Repeated GETs for the same report may return a running‑status object.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack