Load IBM Cloud Hyper Protect Crypto Services data in Python using dltHub

Build a IBM Cloud Hyper Protect Crypto Services-to-database or-dataframe pipeline in Python using dlt with automatic Cursor support.

In this guide, we'll set up a complete Hyper Protect Crypto Services 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 hyper_protect_crypto_migrations_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://uko.us-south.hs-crypto.cloud.ibm.com:9549/v2/",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            types,,query,,search
            ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • Types: This endpoint retrieves various types within the service.
  • Query: Used to execute queries against the service.
  • Search: Allows searching for specific resources.
  • Folders: Provides access to folder structures within the service.
  • Keys: This endpoint manages key-related operations.
  • Processes: Manages different processes within the service environment.
  • Users: Manages user information within the service.

You will then debug the Hyper Protect Crypto Services 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 Hyper Protect Crypto Services support.

    dlt init dlthub:hyper_protect_crypto_migrations 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 Hyper Protect Crypto Services API, as specified in @hyper_protect_crypto_migrations-docs.yaml 
Start with endpoints types and  and skip incremental loading for now. 
Place the code in hyper_protect_crypto_migrations_pipeline.py and name the pipeline hyper_protect_crypto_migrations_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 hyper_protect_crypto_migrations_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    IBM Cloud Identity and Access Management (IAM) is the primary method to authenticate to the Hyper Protect Crypto Services API using OAuth2 access tokens.

    To get the appropriate API keys, please visit the original source at https://www.ibm.com/cloud/hyper-protect-crypto-services. 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 hyper_protect_crypto_migrations_pipeline.py

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

    Pipeline hyper_protect_crypto_migrations load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset hyper_protect_crypto_migrations_data
The duckdb destination used duckdb:/hyper_protect_crypto_migrations.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 hyper_protect_crypto_migrations_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("hyper_protect_crypto_migrations_pipeline").dataset()
# get ype table as Pandas frame
data.ype.df().head()

Running into errors?

Ensure all requests use SSL as plain-text requests will be rejected. Each endpoint may require specific parameters, such as the Workflow definition id and GRC Object id. Some features may be in early stages and not fully supported in all regions. Be cautious when altering networking settings, as changes are permanent. Rate limits may apply, and incorrect IAM tokens could lead to unauthorized errors.

Extra resources:

Next steps