Load Dell PowerScale OneFS data in Python using dltHub

Dell PowerScale OneFS REST API provides programmatic access for managing system configuration and file system operations on PowerScale clusters. The REST API base URL is https://<cluster-ip-or-host-name>:8080 and Supports HTTP Basic authentication or session-based authentication using cookies..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv add "dlt[hub]" and start loading Dell PowerScale OneFS data in under 10 minutes.

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What data can I load from Dell PowerScale OneFS?

Here are some of the endpoints you can load from Dell PowerScale OneFS:

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How do I authenticate with the Dell PowerScale OneFS API?

The API supports HTTP Basic authentication or session-based authentication. For session-based, send a POST to /session/1/session to obtain an 'isisessid' session cookie and an optional 'isicsrf' anti-CSRF token; subsequent requests require the 'Cookie' header containing 'isisessid' and, if applicable, the 'X-CSRF-Token' header.

1. Get your credentials

The Dell PowerScale OneFS REST API does not utilize static API keys. Instead, it uses session-based authentication. To obtain credentials, send an HTTP POST request to the /session/1/session endpoint with a JSON body containing your username, password, and the requested services (platform for configuration or namespace for file access). The response will include a Set-Cookie header containing an isisessid value. Use this isisessid in the Cookie header of all subsequent API requests.

2. Add them to .dlt/secrets.toml

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[sources.dell_powerscale_onefs_source]
username = "your_username_here"
password = "your_password_here"
# The session_id (isisessid) is dynamic and obtained via POST /session/1/session
# session_id = "..."

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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uv run dlthub ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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uv run dlthub ai toolkit install rest-api-pipeline

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Dell PowerScale OneFS API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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uv run python dell_powerscale_onefs_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline dell_powerscale_onefs_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset dell_powerscale_onefs_data
The duckdb destination used duckdb:/dell_powerscale_onefs.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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uv run dlthub show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads /session/1/session and /platform from the Dell PowerScale OneFS API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def dell_powerscale_onefs_source(username=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<cluster-ip-or-host-name>:8080",
            "auth": {"type": "api_key", "api_key": username, "name": "isisessid"},
        },
        "resources": [
            {"name": "smb_shares", "endpoint": {"path": "platform/latest/protocols/smb/shares", "data_selector": "shares"}},
            {"name": "snapshots", "endpoint": {"path": "platform/latest/snapshot/snapshots", "data_selector": "snapshots"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="dell_powerscale_onefs_pipeline",
        destination="duckdb",
        dataset_name="dell_powerscale_onefs_data",
    )
    load_info = pipeline.run(dell_powerscale_onefs_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("dell_powerscale_onefs_pipeline").dataset()
sessions_df = data.smb_shares.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM dell_powerscale_onefs_data.smb_shares LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("dell_powerscale_onefs_pipeline").dataset()
data.smb_shares.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Dell PowerScale OneFS data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack