Load Census data in Python using dltHub

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

In this guide, we'll set up a complete Census 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 census_migrations_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.getcensus.com/v1/",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            syncs, events, contacts
            ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • Syncs: Manage data synchronization between various data sources.
  • Events: Handle event data related to user actions and company activities.
  • Contacts: Manage contact information within the system.
  • Datasets: Work with different datasets for data management and analysis.
  • Campaigns: Manage marketing campaigns and their associated data.

You will then debug the Census 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 Census support.

    dlt init dlthub:census_migrations 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 Census API, as specified in @census_migrations-docs.yaml 
Start with endpoints syncs and  and skip incremental loading for now. 
Place the code in census_migrations_pipeline.py and name the pipeline census_migrations_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 census_migrations_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    Census uses OAuth2 for authentication and requires a refresh token flow for accessing secure endpoints. Ensure that the OAuth application is properly configured, including setting the necessary credentials.

    To get the appropriate API keys, please visit the original source at https://www.getcensus.com/. 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 census_migrations_pipeline.py

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

    Pipeline census_migrations load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset census_migrations_data
The duckdb destination used duckdb:/census_migrations.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 census_migrations_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("census_migrations_pipeline").dataset()
# get ync table as Pandas frame
data.ync.df().head()

Running into errors?

Census requires a connected app setup for OAuth2 authentication. Rate limits apply to API calls, and some endpoints may return incomplete or null values in nested fields. Incremental syncs are recommended to optimize performance. It's important to add filters to manage data volume effectively and consider using CSV datasets for static data. Also, be mindful that some features may not support real-time data updates.

Extra resources:

Next steps