Uscreen Python API Docs | dltHub

Uscreen is a video monetization and OTT platform providing APIs to manage customers, videos, access controls, analytics and store data. The REST API base URL is https://www.uscreen.io/publisher_api/v1 and API key required in the Authorization header.

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 Uscreen data in under 10 minutes.

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What data can I load from Uscreen?

Here are some of the endpoints you can load from Uscreen:

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How do I authenticate with the Uscreen API?

API access is granted via an API authorization key. Include the key in the Authorization header when calling the Publisher API.

1. Get your credentials

1. Ensure you are on the UscreenPlus plan or upgrade to it.
2. Contact your Customer Success Manager or email sales@uscreen.tv to request API access/authorization key.
3. Once provisioned, receive the API authorization key from Uscreen support/CSM and paste it into the Authorization header in API clients or the Swagger "Authorize" dialog.

2. Add them to .dlt/secrets.toml

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[sources.uscreen_source]
api_key = "your_uscreen_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 Uscreen 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 uscreen_pipeline.py

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

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Pipeline uscreen_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset uscreen_data
The duckdb destination used duckdb:/uscreen.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 uscreen_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 customers and videos from the Uscreen 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 uscreen_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.uscreen.io/publisher_api/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "customers", "endpoint": {"path": "customers"}},
            {"name": "videos", "endpoint": {"path": "videos"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="uscreen_pipeline",
        destination="duckdb",
        dataset_name="uscreen_data",
    )
    load_info = pipeline.run(uscreen_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("uscreen_pipeline").dataset()
sessions_df = data.customers.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM uscreen_data.customers LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("uscreen_pipeline").dataset()
data.customers.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 Uscreen 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

Authentication failures

If you are missing or using an invalid API key the API will reject requests. Verify you received an API authorization key from your Customer Success Manager, and include it in the Authorization header (use the Swagger "Authorize" dialog to test). If access is not enabled, contact sales@uscreen.tv or your CSM to confirm your account is on the UscreenPlus plan and that API access is provisioned.

Rate limits and throttling

The public Publisher API documentation does not publish explicit rate limit numbers. Design clients to handle 429 responses (backoff and retry with exponential delay), and paginate large queries. If you encounter rate limiting for production workloads, contact Uscreen support to request quota or guidance.

Pagination quirks

Most list endpoints in the Publisher API are paginated. Use the pagination parameters returned by each endpoint (inspect the Swagger responses for page, per_page, total or related fields). If results are truncated, iterate pages until no further pages are returned.

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