Load BigPanda data in Python using dltHub

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

In this guide, we'll set up a complete BigPanda 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 bigpanda_scim_users_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://integrations.bigpanda.io/oim/splunk_v2",
            "auth": {
                "type": "bearer",
                "token": access_token,
            }
        },
        "resources": [
            "alerts", "alerts`."
        ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • PagerDuty Webhook: Endpoint for receiving alerts from PagerDuty.
  • Zabbix Alerts: Endpoint for sending alerts from Zabbix monitoring system.
  • AriaOps Alerts: Endpoint for sending alerts from AriaOps, requires an access token.
  • ThousandEyes Alerts: Endpoint for receiving alerts from ThousandEyes, requires an app key.
  • Sumo Logic Alerts: Endpoint for alerts from Sumo Logic monitoring platform.
  • SQL Sentry Alerts: Endpoint for alerts from SQL Sentry, requires an access token.
  • Splunk Alerts: Endpoint for receiving alerts from Splunk.
  • SolarWinds Alerts: Endpoint for alerts from SolarWinds, requires an access token.
  • SiteScope Alerts: Endpoint for alerts from SiteScope, requires an app key.
  • Scom Alerts: Endpoint for alerts from System Center Operations Manager (SCOM).
  • ScienceLogic Alerts: Endpoint for alerts from ScienceLogic, requires an app key.
  • Prometheus Alerts: Endpoint for alerts from Prometheus monitoring system, can include access token.
  • New Relic Alerts: Endpoint for alerts from New Relic monitoring platform.
  • LogScale Alerts: Endpoint for alerts from LogScale, requires an access token.
  • LogicMonitor Alerts: Endpoint for alerts from LogicMonitor, requires an app key.
  • Grafana Alerts: Endpoint for alerts from Grafana, requires an access token.

You will then debug the BigPanda 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 BigPanda support.

    dlt init dlthub:bigpanda_scim_users 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 BigPanda API, as specified in @bigpanda_scim_users-docs.yaml 
Start with endpoints alerts and alerts`. and skip incremental loading for now. 
Place the code in bigpanda_scim_users_pipeline.py and name the pipeline bigpanda_scim_users_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 bigpanda_scim_users_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    You will need an API Key, which can be created in BigPanda; ensure you have it for integration purposes.

    To get the appropriate API keys, please visit the original source at https://docs.bigpanda.io/en/scim-users-api.html. 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 bigpanda_scim_users_pipeline.py

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

    Pipeline bigpanda_scim_users load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset bigpanda_scim_users_data
The duckdb destination used duckdb:/bigpanda_scim_users.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 bigpanda_scim_users_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("bigpanda_scim_users_pipeline").dataset()
# get "alerts" table as Pandas frame
data."alerts".df().head()

Extra resources:

Next steps