Cast Software Python API Docs | dltHub

CAST Dashboards / RestAPI provides access to data related to applications, modules, and snapshots, allowing for the discovery of resource representations at runtime. The REST API base URL is The base URL is deployment-specific; an example is 'http://localhost:9090/Dashboard-WebService/rest/'. Users must construct the base_url from their deployed Dashboard WebService root and append the rest URI prefix (commonly '/rest/' or '/com.castsoftware.aip.dashboard.../rest/' for WAR packaging). and Requests can be authenticated using either Basic Authentication with a username and password or an API key with 'X-API-KEY' and 'X-API-USER' headers..

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 pip install "dlt[workspace]" and start loading Cast Software data in under 10 minutes.

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What data can I load from Cast Software?

Here are some of the endpoints you can load from Cast Software:

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How do I authenticate with the Cast Software API?

Authentication supports Basic Authentication, which involves using GET /.../rest/login with an 'Authorization: Basic ' header, and API key authentication, which requires 'X-API-KEY' and 'X-API-USER' headers.

1. Get your credentials

To obtain API credentials, configure the 'security.apikey' property in the 'application.properties' file of your CAST Dashboards deployment. The location of this file varies depending on the deployment type (WAR, ZIP, JAR, Docker). After setting the API key, restart the server for the changes to take effect. The API key will then be used with 'X-API-KEY' and 'X-API-USER' headers.

2. Add them to .dlt/secrets.toml

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[sources.cast_software_source]
api_key = "your_api_key_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 venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt 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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dlt ai toolkit rest-api-pipeline install

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 Cast Software 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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python cast_software_pipeline.py

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

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Pipeline cast_software_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset cast_software_data
The duckdb destination used duckdb:/cast_software.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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dlt pipeline cast_software_pipeline 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 applications and modules from the Cast Software 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 cast_software_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "The base URL is deployment-specific; an example is 'http://localhost:9090/Dashboard-WebService/rest/'. Users must construct the base_url from their deployed Dashboard WebService root and append the rest URI prefix (commonly '/rest/' or '/com.castsoftware.aip.dashboard.../rest/' for WAR packaging).",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "applications", "endpoint": {"path": "AAD/applications", "data_selector": "applications"}},
            {"name": "modules", "endpoint": {"path": "AAD/modules", "data_selector": "modules"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="cast_software_pipeline",
        destination="duckdb",
        dataset_name="cast_software_data",
    )
    load_info = pipeline.run(cast_software_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("cast_software_pipeline").dataset()
sessions_df = data.applications.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM cast_software_data.applications LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("cast_software_pipeline").dataset()
data.applications.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 Cast Software 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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Troubleshooting

Common API Errors

The CAST Dashboards REST API documents several common HTTP status codes and their corresponding error messages. When an error occurs, the API typically returns a JSON object with a 'code' and 'message' field.

HTTP Status Codes:

• 400 Bad Request: Indicates a client-side error, often with subcodes for more specific issues.
• 401 Unauthorized: Authentication is required or has failed.
• 403 Forbidden: The server understood the request but refuses to authorize it.
• 404 Not Found: The requested resource could not be found.
• 406 Not Acceptable: The server cannot produce a response matching the list of acceptable values defined in the request's proactive content negotiation headers.
• 500 Internal Server Error: A generic error message, given when an unexpected condition was encountered and no more specific message is suitable.
• 503 Service Unavailable: The server is currently unable to handle the request due to a temporary overload or scheduled maintenance.

Example Error JSON:

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{
    "code": 1,
    "message": "Cannot process this request"
}

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

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

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

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• 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