Load Infoblox WAPI data in Python using dltHub

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

In this guide, we'll set up a complete Infoblox WAPI 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 infoblox_wapi_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://127.0.0.1/wapi/v2.13.7/",
            "auth": {
                "type": "basic",
                "username": username,
                "password": password,
            },
        },
        "resources": [
            admingroup
        ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • Admin Groups: Manage administrative groups and their permissions
  • Authentication: Handle user login, logout, and session management
  • Configuration: Retrieve and modify system settings and configurations
  • Network Objects: Manage DNS records, zones, and other network-related resources
  • Users: Create, update, and manage user accounts and credentials
  • Monitoring: Access system logs, events, and performance metrics
  • Licensing: View and manage license information and usage

You will then debug the Infoblox WAPI 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 Infoblox WAPI support.

    dlt init dlthub:infoblox_wapi 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 Infoblox WAPI API, as specified in @infoblox_wapi-docs.yaml 
Start with endpoint(s) admingroup and skip incremental loading for now. 
Place the code in infoblox_wapi_pipeline.py and name the pipeline infoblox_wapi_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 infoblox_wapi_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    The API uses HTTP Basic Authentication with the realm "InfoBlox ONE Platform". Credentials must be sent in the Authorization header using the Basic scheme (base64-encoded username:password). The server sets an ibapauth cookie for session management after successful authentication.

    To get the appropriate API keys, please visit the original source at ipam.illinois.edu. 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 infoblox_wapi_pipeline.py

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

    Pipeline infoblox_wapi load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset infoblox_wapi_data
The duckdb destination used duckdb:/infoblox_wapi.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 infoblox_wapi_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("infoblox_wapi_pipeline").dataset()
# get admingroup table as Pandas frame
data.admingroup.df().head()

Extra resources:

Next steps