Load Follow up boss data in Python using dltHub

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

In this guide, we'll set up a complete Follow Up Boss 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 follow_up_boss_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.followupboss.com/v1/",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            "people",
            "events",
            "email_campaigns"
            ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • People: Manage contacts and their details.
  • Events: Create and manage events related to leads.
  • Email Campaigns: Handle email marketing campaigns and their updates.
  • Users: Access user information and settings.
  • Team Inboxes: Manage team inbox settings and configurations.

You will then debug the Follow Up Boss 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 Follow Up Boss support.

    dlt init dlthub:follow_up_boss 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 Follow Up Boss API, as specified in @follow_up_boss-docs.yaml 
Start with endpoints "people" and "events" and skip incremental loading for now. 
Place the code in follow_up_boss_pipeline.py and name the pipeline follow_up_boss_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 follow_up_boss_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    Authentication is handled via OAuth2, requiring the use of an access token that is included in the request header. The token is short-lived and can be refreshed using a refresh token.

    To get the appropriate API keys, please visit the original source at https://www.followupboss.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 follow_up_boss_pipeline.py

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

    Pipeline follow_up_boss load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset follow_up_boss_data
The duckdb destination used duckdb:/follow_up_boss.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 follow_up_boss_pipeline show --dashboard

  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("follow_up_boss_pipeline").dataset()
# get "people" table as Pandas frame
data.people.df().head()

Running into errors?

When creating events for new leads, push notifications to cell phones are not initiated by Follow Up Boss. Additionally, certain endpoints like /people should not be used for adding new leads, as they won't trigger automations. Ensure to include required system headers for integration and handle API keys securely.

Extra resources:

Next steps