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Load Trend Micro Apex One data in Python using dltHub

Build a Trend Micro Apex One-to-database or-dataframe pipeline in Python using dlt with automatic Cursor support.

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In this guide, we'll set up a complete Epiphany Intelligence Platform 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 trend_micro_apex_one_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://reveald.com/v",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            "upload", "api/user", "api/data"
            ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • upload: Handles file uploads to the platform.
  • user: Manages user-related data and operations.
  • data: Accesses various data sets for analysis.
  • alert: Retrieves or manages alerts in the system.
  • machine: Interfaces with machine data and statistics.

You will then debug the Epiphany Intelligence Platform 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 Epiphany Intelligence Platform support.

    dlt init dlthub:trend_micro_apex_one 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 Epiphany Intelligence Platform API, as specified in @trend_micro_apex_one-docs.yaml 
Start with endpoints upload and  and skip incremental loading for now. 
Place the code in trend_micro_apex_one_pipeline.py and name the pipeline trend_micro_apex_one_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 trend_micro_apex_one_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    The Epiphany Intelligence Platform uses OAuth2 for authentication, requiring a setup of a connected application in the API. An access token needs to be obtained and managed, and the application must be granted appropriate permissions.

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

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

    Pipeline trend_micro_apex_one load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset trend_micro_apex_one_data
The duckdb destination used duckdb:/trend_micro_apex_one.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 trend_micro_apex_one_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("trend_micro_apex_one_pipeline").dataset()
# get ploa table as Pandas frame
data.ploa.df().head()

Running into errors?

Ensure that the application name is unique and adhere to the recommended naming conventions. The platform requires setup of connected apps for OAuth2, and permissions need to be granted for changes to take effect. Some objects may return nulls in deeply nested fields, and specific roles, such as global reader privileges, are necessary for data collection.

Extra resources:

Next steps

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