Load FEWS NET data in Python using dltHub

Build a FEWS NET-to-database or-dataframe pipeline in Python using dlt with automatic Cursor support.

In this guide, we'll set up a complete FEWS NET data pipeline from API credentials to your first data load in just 10 minutes. You'll end up with a fully declarative Python pipeline based on dlt's REST API connector, like in the partial example code below:

Example code
@dlt.source
def fews_net_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://fdw.fews.net/api/",
            "auth": {
                "type": "bearer",
                "token": "jwt_token",
            },
        },
        "resources": [
            feature, feature.geojson
        ],
    }
    [...]
    yield from rest_api_resources(config)


def get_data() -> None:
    # Connect to destination
    pipeline = dlt.pipeline(
        pipeline_name='fews_net_pipeline',
        destination='duckdb',
        dataset_name='fews_net_data', 
    )
    # Load the data
    load_info = pipeline.run(fews_net_source())
    print(load_info)

Why use dltHub Workspace with LLM Context to generate Python pipelines?

  • Accelerate pipeline development with AI-native context
  • Debug pipelines, validate schemas and data with the integrated Pipeline Dashboard
  • Build Python notebooks for end users of your data
  • Low maintenance thanks to Schema evolution with type inference, resilience and self documenting REST API connectors. A shallow learning curve makes the pipeline easy to extend by any team member
  • dlt is the tool of choice for Pythonic Iceberg Lakehouses, bringing mature data loading to pythonic Iceberg with or without catalogs

What you’ll do

We’ll show you how to generate a readable and easily maintainable Python script that fetches data from fews_net’s API and loads it into Iceberg, DataFrames, files, or a database of your choice. Here are some of the endpoints you can load:

  • Feature Data: Retrieve geographic features in GeoJSON format with filtering options by country code, unit type (administrative divisions, livelihood zones), and date
  • Administrative Units: Access food security and livelihood zone administrative boundary data for specified countries and time periods

You will then debug the FEWS NET pipeline using our Pipeline Dashboard tool to ensure it is copying the data correctly, before building a Notebook to explore your data and build reports.

Setup & steps to follow

💡
Before getting started, let's make sure Cursor is set up correctly:

Now you're ready to get started!

  1. ⚙️ Set up dlt Workspace

    Install dlt with duckdb support:

    pip install dlt[workspace]

    Initialize a dlt pipeline with FEWS NET support.

    dlt init dlthub:fews_net duckdb

    The init command will setup the necessary files and folders for the next step.

  2. 🤠 Start LLM-assisted coding

    Here’s a prompt to get you started:

    Prompt
    Please generate a REST API Source for FEWS NET API, as specified in @fews_net-docs.yaml 
Start with endpoint(s) feature and feature.geojson and skip incremental loading for now. 
Place the code in fews_net_pipeline.py and name the pipeline fews_net_pipeline. 
If the file exists, use it as a starting point. 
Do not add or modify any other files. 
Use @dlt rest api as a tutorial. 
After adding the endpoints, allow the user to run the pipeline with python fews_net_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    The FDW REST API supports two authentication methods. Session Authentication uses a POST request to /accounts/login/ with username and password, returning a session cookie for subsequent requests. Token Authentication uses a POST request to /api-token-auth/ with username and password, returning a JWT token that can be sent as an HTTP header Authorization: JWT or as a query parameter ?jwt=. Requests without authentication may succeed but return only public data.

    To get the appropriate API keys, please visit the original source at help.fews.net. If you want to protect your environment secrets in a production environment, look into setting up credentials with dlt.

  4. 🏃‍♀️ Run the pipeline in the Python terminal in Cursor

    python fews_net_pipeline.py

    If your pipeline runs correctly, you’ll see something like the following:

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

  5. 📈 Debug your pipeline and data with the Pipeline Dashboard

    Now that you have a running pipeline, you need to make sure it’s correct, so you do not introduce silent failures like misconfigured pagination or incremental loading errors. By launching the dlt Workspace Pipeline Dashboard, you can see various information about the pipeline to enable you to test it. Here you can see:

    • Pipeline overview: State, load metrics
    • Data’s schema: tables, columns, types, hints
    • You can query the data itself
    dlt pipeline fews_net_pipeline show

  6. 🐍 Build a Notebook with data explorations and reports

    With the pipeline and data partially validated, you can continue with custom data explorations and reports. To get started, paste the snippet below into a new marimo Notebook and ask your LLM to go from there. Jupyter Notebooks and regular Python scripts are supported as well.

    import dlt

data = dlt.pipeline("fews_net_pipeline").dataset()
# get feature table as Pandas frame
data.feature.df().head()

Extra resources:

Next steps