Load Global Forest Watch data to DuckDB

Global Forest Watch (GFW) Data API allows developers to explore, manage, and access forest-related geospatial data programmatically. Everything needed to build a working Global Forest Watch → 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 Global Forest Watch 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 Global Forest Watch 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 Global Forest Watch 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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Global Forest Watch API at a glance

These values come from the Global Forest Watch API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Global Forest Watch API?

Authentication is performed by including an access token in the Authorization header as a Bearer token, formatted as 'Authorization: Bearer '.

1. Get your credentials

1. Create a Global Forest Watch account at the official sign-up page (https://api.resourcewatch.org/auth/sign-up). 2. Use your credentials to generate a JWT access token by sending a POST request to the /auth/token endpoint. 3. Exchange this JWT access token for a production API key by sending a POST request to https://data-api.globalforestwatch.org/auth/apikey with the Authorization: Bearer header and a JSON body containing your alias, email, organization, and optional domains list. Securely store the returned API key.

2. Add them to .dlt/secrets.toml

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[sources.global_forest_watch_source]
gfw_api_key = "your_api_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 Global Forest Watch data can I load into DuckDB?

These are the Global Forest Watch endpoints dlt can load into DuckDB:

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How do I load only new Global Forest Watch records?

Global Forest Watch exposes page[number] on datasets, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "datasets", "endpoint": {
    "path": "datasets",
    "data_selector": "data",
    "incremental": {"cursor_path": "page[number]", "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 Global Forest Watch pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /dataset and /auth/apikey from the Global Forest Watch API into DuckDB:

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

@dlt.source
def global_forest_watch_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://data-api.globalforestwatch.org",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "datasets", "endpoint": {"path": "datasets", "data_selector": "data"}},
            {"name": "version_assets", "endpoint": {"path": "dataset/{dataset}/{version}/assets", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_global_forest_watch_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="global_forest_watch_pipeline",
        destination="duckdb",
        dataset_name="global_forest_watch_data",
    )
    load_info = pipeline.run(global_forest_watch_source())
    print(load_info)


if __name__ == "__main__":
    load_global_forest_watch_to_duckdb()

Run it with python global_forest_watch_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 Global Forest Watch 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("global_forest_watch_pipeline").dataset()
df = data.datasets.df()
print(df.head())

SQL:

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SELECT * FROM global_forest_watch_data.datasets LIMIT 10;

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

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How do I deploy the Global Forest Watch 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 Global Forest Watch 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 Global Forest Watch 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