Load Cisco Crosswork NSO data in Python using dltHub

Cisco Crosswork NSO REST API provides a RESTCONF-compliant interface for managing network services and orchestration via the Crosswork Network Controller or directly through NSO. The REST API base URL is https://{cnc_host}:{cnc_port}/crosswork/proxy/nso/restconf/ and All requests require a Bearer token when routed through the Crosswork API Gateway, or HTTP Basic authentication when accessing NSO directly..

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 Cisco Crosswork NSO data in under 10 minutes.

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What data can I load from Cisco Crosswork NSO?

Here are some of the endpoints you can load from Cisco Crosswork NSO:

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How do I authenticate with the Cisco Crosswork NSO API?

When accessed via Crosswork Network Controller (CNC), authenticate using the CNC SSO server to obtain a JWT, which must be passed in the 'Authorization' header as a Bearer token (e.g., 'Authorization: Bearer {JWT}'). For direct NSO RESTCONF API access, HTTP Basic authentication is standard.

1. Get your credentials

Cisco Crosswork (CNC) uses a two-step authentication process to obtain a JSON Web Token (JWT), which is then used as a bearer token for API calls. First, perform a POST request to the Single Sign-On (SSO) server at https://{cnc_host}:{cnc_port}/crosswork/sso/v1/tickets with a payload of username={username}&password={password} to obtain a Ticket Granting Ticket (TGT). Second, use the TGT to request a JWT by performing a POST request to https://{cnc_host}:{cnc_port}/crosswork/sso/v1/tickets/jwt with a payload of tgt={TGT}&service={service_url}. The resulting jwttoken is used as the Bearer token in the Authorization header for all subsequent API requests. Note that NSO standalone RESTCONF uses standard HTTP Basic Authentication (username

).

2. Add them to .dlt/secrets.toml

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[sources.cisco_crosswork_nso_source]
cnc_host = "your_cnc_host"
cnc_port = "your_cnc_port"
username = "your_username"
password = "your_password"
# Alternatively, if using a pre-obtained token
# access_token = "your_jwt_token_here"

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 Cisco Crosswork NSO 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 cisco_crosswork_nso_pipeline.py

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

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Pipeline cisco_crosswork_nso_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset cisco_crosswork_nso_data
The duckdb destination used duckdb:/cisco_crosswork_nso.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 '/crosswork/proxy/nso/restconf/data and /crosswork/proxy/nso/restconf/data/ietf-restconf-monitoring

/streams' from the Cisco Crosswork NSO 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 cisco_crosswork_nso_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{cnc_host}:{cnc_port}/crosswork/proxy/nso/restconf/",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "devices", "endpoint": {"path": "restconf/data/tailf-ncs:devices", "data_selector": "tailf-ncs:devices"}},
            {"name": "services", "endpoint": {"path": "restconf/data/tailf-ncs:services", "data_selector": "tailf-ncs:services"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="cisco_crosswork_nso_pipeline",
        destination="duckdb",
        dataset_name="cisco_crosswork_nso_data",
    )
    load_info = pipeline.run(cisco_crosswork_nso_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("cisco_crosswork_nso_pipeline").dataset()
sessions_df = data.devices.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM cisco_crosswork_nso_data.devices LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("cisco_crosswork_nso_pipeline").dataset()
data.devices.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 Cisco Crosswork NSO 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