Load Drata data in Python using dltHub

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

In this guide, we'll set up a complete JumpCloud 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 drata_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://console.jumpcloud.com/",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            "personnel",
            "events",
            "controls"
            ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • Personnel: Retrieves personnel records.
  • Events: Fetches public events sorted by creation date.
  • Controls: Accesses control records.
  • GCP APIs: Lists GCP APIs available.
  • Logs: Provides access to logs, including security and archive logs.
  • Incident Reports: Retrieves incident reports and responses.
  • Compliance Policies: Manages macOS compliance policies.

You will then debug the JumpCloud 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 JumpCloud support.

    dlt init dlthub:drata 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 JumpCloud API, as specified in @drata-docs.yaml 
Start with endpoints "personnel" and "events" and skip incremental loading for now. 
Place the code in drata_pipeline.py and name the pipeline drata_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 drata_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    The authentication requires an API key, which must be included in the header under the name 'Authorization'. The token can be obtained via a specific token URL using the client ID and client secret.

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

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

    Pipeline drata load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset drata_data
The duckdb destination used duckdb:/drata.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 drata_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("drata_pipeline").dataset()
# get "personnel" table as Pandas frame
data.personnel.df().head()

Running into errors?

Users must be aware that API tokens should be treated with care as they carry the same privileges as the user. Tokens cannot be shared across workspaces and have a set expiration date. Additionally, a rate limit of 500 requests per minute applies. There may also be delays in syncing data from Microsoft APIs, and specific permissions and settings are required for successful integration.

Extra resources:

Next steps