Load FDA Data Dashboard data in Python using dltHub

Build a FDA Data Dashboard-to-database or-dataframe pipeline in Python using dlt with automatic Cursor support.

In this guide, we'll set up a complete FDA Data Dashboard 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 fda_data_dashboard_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api-datadashboard.fda.gov/v1/",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            inspections_classifications
        ],
    }
    [...]
    yield from rest_api_resources(config)


def get_data() -> None:
    # Connect to destination
    pipeline = dlt.pipeline(
        pipeline_name='fda_data_dashboard_pipeline',
        destination='duckdb',
        dataset_name='fda_data_dashboard_data', 
    )
    # Load the data
    load_info = pipeline.run(fda_data_dashboard_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 fda_data_dashboard’s API and loads it into Iceberg, DataFrames, files, or a database of your choice. Here are some of the endpoints you can load:

  • Inspections & Classifications: Access data on FDA inspections and establishment classifications
  • Data Dashboard: General data retrieval and analytics endpoints for FDA regulatory information

You will then debug the FDA Data Dashboard 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 FDA Data Dashboard support.

    dlt init dlthub:fda_data_dashboard 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 FDA Data Dashboard API, as specified in @fda_data_dashboard-docs.yaml 
Start with endpoint(s) inspections_classifications and skip incremental loading for now. 
Place the code in fda_data_dashboard_pipeline.py and name the pipeline fda_data_dashboard_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 fda_data_dashboard_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    API credentials consist of an Authorization-User (email address) and an FDA-generated Authorization-Key, both of which must be passed as custom headers (Authorization-User and Authorization-Key respectively) in every API request. The Authorization header format requires Content-Type: application/json, Authorization-User: , and Authorization-Key: in the request headers, and TLS 1.2 is required for all API requests.

    To get the appropriate API keys, please visit the original source at datadashboard.fda.gov. 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 fda_data_dashboard_pipeline.py

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

    Pipeline fda_data_dashboard load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset fda_data_dashboard_data
The duckdb destination used duckdb:/fda_data_dashboard.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 fda_data_dashboard_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("fda_data_dashboard_pipeline").dataset()
# get inspections_classifications table as Pandas frame
data.inspections_classifications.df().head()

Extra resources:

Next steps