Load Zerobounce data in Python using dltHub

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

In this guide, we'll set up a complete ZeroBounce 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 zerobounce_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://bulkapi.zerobounce.net/v2/",
            "auth": {
                "type": "apikey",
                "token": access_token,
            },
        },
        "resources": [
            "validation",
            "getcredits",
            "sendfile"
            ],
    }
    [...]
    yield from rest_api_resources(config)


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

Email Validation: Validates email addresses for accuracy and deliverability.
Validate Emails: Checks the validity of emails using the latest validation methods.
Get Credit Balance: Retrieves the remaining credit balance for email verification.
Send File: Allows users to upload a file containing email addresses for batch processing.
File Status: Provides the current status of a processed file.
Get File: Retrieves the results of a previously processed file.
Delete File: Removes a previously uploaded file from the system.
Activity: Monitors the activity logs related to email validation requests.
Email Finder: Searches for email addresses based on provided company and domain information.

You will then debug the ZeroBounce 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:

We suggest using a model like Claude 3.7 Sonnet or better
Index the REST API Source tutorial: https://dlthub.com/docs/dlt-ecosystem/verified-sources/rest_api/ and add it to context as @dlt rest api
Read our full steps on setting up Cursor

Now you're ready to get started!

⚙️ Set up dlt Workspace

Install dlt with duckdb support:
```
pip install dlt[workspace]
```
Initialize a dlt pipeline with ZeroBounce support.
```
dlt init dlthub:zerobounce duckdb
```
The init command will setup the necessary files and folders for the next step.

🤠 Start LLM-assisted coding

Here’s a prompt to get you started:

Prompt
Please generate a REST API Source for ZeroBounce API, as specified in @zerobounce-docs.yaml 
Start with endpoints "validation" and "getcredits" and skip incremental loading for now. 
Place the code in zerobounce_pipeline.py and name the pipeline zerobounce_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 zerobounce_pipeline.py and await further instructions.

🔒 Set up credentials

Authentication is done via an API key, which must be included in the request headers or as a query parameter. The API key is essential for accessing the service and retrieving data.

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

🏃‍♀️ Run the pipeline in the Python terminal in Cursor


python zerobounce_pipeline.py

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


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

📈 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 zerobounce_pipeline show --dashboard
```
🐍 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("zerobounce_pipeline").dataset()
# get "validation" table as Pandas frame
data.validation.df().head()
```

Running into errors?

Users must adhere to rate limits, which are set at 5 uses per minute. Exceeding this limit may lead to temporary blocks. Additionally, the service may require a minimum of 100 emails for batch processing and could store customer data for a limited period. It's also important to ensure that proper email format is followed to avoid errors.

Extra resources:

Learn more with our 1h LLM-assisted coding course!