Create a pipeline

Follow the steps below to create a pipeline from the WeatherAPI.com API to DuckDB from scratch. The same steps can be repeated for any source and destination of your choice—use dlt init <source> <destination> and then build the pipeline for that API instead.

Please make sure you have installed dlt before following the steps below.

1. Initialize project

Create a new empty directory for your dlt project by running:

mkdir weatherapi_duckdb && cd weatherapi_duckdb

Start a dlt project with a pipeline template that loads data to DuckDB by running:

dlt init weatherapi duckdb

Install the dependencies necessary for DuckDB:

pip install -r requirements.txt

2. Add WeatherAPI.com API credentials

You will need to sign up for the WeatherAPI.com API.

Once you do this, you should see your API Key at the top of your user page.

Copy the value of the API key into .dlt/secrets.toml:

[sources]
api_secret_key = '<api key value>'

The secret name must correspond to the argument name in the source, i.e. api_secret_key=dlt.secrets.value in def weatherapi_source(api_secret_key=dlt.secrets.value):.

Run the weatherapi.py pipeline script to test that authentication headers look fine:

python3 weatherapi.py

Your API key should be printed out to stdout along with some test data.

3. Request data from the WeatherAPI.com API

Replace the definition of the weatherapi_resource function definition in the weatherapi.py pipeline script with a call to the WeatherAPI.com API:

@dlt.resource(write_disposition="append")
def weatherapi_resource(api_secret_key=dlt.secrets.value):
    url = "https://api.weatherapi.com/v1/current.json"
    params = {
        "q": "NYC",
        "key": api_secret_key
    }
    response = requests.get(url, params=params)
    response.raise_for_status()
    yield response.json()

Run the weatherapi.py pipeline script to test that the API call works:

python3 weatherapi.py

This should print out the weather in New York City right now.

4. Load the data

Remove the exit() call from the main function in weatherapi.py, so that running the python3 weatherapi.py command will now also run the pipeline:

if __name__=='__main__':

    # configure the pipeline with your destination details
    pipeline = dlt.pipeline(
        pipeline_name='weatherapi',
        destination='duckdb',
        dataset_name='weatherapi_data'
    )

    # print credentials by running the resource
    data = list(weatherapi_resource())

    # print the data yielded from resource
    print(data)

    # run the pipeline with your parameters
    load_info = pipeline.run(weatherapi_source())

    # pretty print the information on data that was loaded
    print(load_info)

Run the weatherapi.py pipeline script to load data into DuckDB:

python3 weatherapi.py

Then this command to see that the data loaded:

dlt pipeline weatherapi show

This will open a Streamlit app that gives you an overview of the data loaded.

5. Next steps

Now that you have a working pipeline, you have options for what to learn next:

• Deploy this pipeline with GitHub Actions, so that the data is automatically loaded on a schedule.
• Transform the loaded data with dbt or in Pandas DataFrames.
• Learn how to run, monitor, and alert when you put your pipeline in production.
• Try loading data to a different destination like Google BigQuery, Amazon Redshift, or Postgres.