Load Hitachi Vantara Clear Sight data in Python using dltHub

Build a Hitachi Vantara Clear Sight-to-database or-dataframe pipeline in Python using dlt with automatic Cursor support.

In this guide, we'll set up a complete Hitachi Vantara Clear Sight 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 hitachi_vantara_clear_sight_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "baseURL/clearsightadvanced/dbapi.do",
            "auth": {
                "type": "bearer",
                "token": "access_token",
            },
        },
        "resources": [
            abortRequest, getStatus
        ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • Authentication: Token endpoint for OpenID Connect authentication at /auth/realms/vsp360/protocol/openid-connect/token
  • Request Management: Abort and status checking for API requests via dbapi.do with actions like abortRequest and getStatus
  • Database API: Core data operations through /clearsightadvanced/dbapi.do endpoint for dataset queries and management

You will then debug the Hitachi Vantara Clear Sight 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 Hitachi Vantara Clear Sight support.

    dlt init dlthub:hitachi_vantara_clear_sight 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 Hitachi Vantara Clear Sight API, as specified in @hitachi_vantara_clear_sight-docs.yaml 
Start with endpoint(s) abortRequest and getStatus and skip incremental loading for now. 
Place the code in hitachi_vantara_clear_sight_pipeline.py and name the pipeline hitachi_vantara_clear_sight_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 hitachi_vantara_clear_sight_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    A user submits a REST API request with a valid bearer access token. The VSP 360 OAuth2 Proxy validates the token using Keycloak, and Clear Sight Advanced verifies that the request originates from a service account with the required role permissions.

The bearer access token must be included in the request. The token is validated against Keycloak by the OAuth2 Proxy. Service accounts require appropriate role permissions to access the API.

To get the appropriate API keys, please visit the original source at docs.hitachivantara.com.
If you want to protect your environment secrets in a production environment, look into [setting up credentials with dlt](https://dlthub.com/docs/walkthroughs/add_credentials).

4. 🏃‍♀️ Run the pipeline in the Python terminal in Cursor

```shell
python hitachi_vantara_clear_sight_pipeline.py
```

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

```shell
Pipeline hitachi_vantara_clear_sight load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset hitachi_vantara_clear_sight_data
The duckdb destination used duckdb:/hitachi_vantara_clear_sight.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

```shell
dlt pipeline hitachi_vantara_clear_sight_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.


```python
import dlt

data = dlt.pipeline("hitachi_vantara_clear_sight_pipeline").dataset()

get abortRequest table as Pandas frame

data.abortRequest.df().head() ```

Extra resources:

Next steps