Beamer Python API Docs | dltHub

Info-beamer is a hosted digital signage management platform exposing a versioned HTTP REST API to manage devices, setups, packages, assets, accesses and checks. The REST API base URL is https://info-beamer.com/api/v1/ and Requests require an info-beamer API key (sent via HTTP Basic with empty username or as a Bearer token)..

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 Beamer data in under 10 minutes.

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What data can I load from Beamer?

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

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How do I authenticate with the Beamer API?

API keys (per-access keys, session keys or adhoc keys) are used. Send the API key as the password of HTTP Basic auth with an empty username (or 'api' if client disallows empty username), or send it in the Authorization header as 'Bearer <API_KEY>'. api-key URL parameter is also supported but not recommended.

1. Get your credentials

1. Log in to your info-beamer account dashboard. 2) Open Access / API keys (or Permissions -> Accesses) for the account. 3) Create or copy an existing API key (apply ACL restrictions as needed). 4) For temporary session keys use the sessions or OAuth endpoints to create a session key; for short-lived keys use adhoc API key creation.

2. Add them to .dlt/secrets.toml

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[sources.beamer_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 Beamer 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 beamer_pipeline.py

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

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Pipeline beamer_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset beamer_data
The duckdb destination used duckdb:/beamer.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 beamer_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 devices and setups from the Beamer 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 beamer_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://info-beamer.com/api/v1/",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "devices", "endpoint": {"path": "device/list"}},
            {"name": "setups", "endpoint": {"path": "setup/list"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM beamer_data.devices LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("beamer_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 Beamer 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 receive HTTP 401, verify that you provided a valid API key. When using HTTP Basic auth, the username must be empty (or use 'api') and the API key placed as the password. Alternatively use 'Authorization: Bearer <API_KEY>'.

Rate limits and 429 responses

The API enforces a global sustained limit (300 calls/min per account) and per-endpoint limits (examples: device:list and setup:list ~15/min). Exceeding limits returns HTTP 429 and a Retry-After header; implement exponential backoff and honor Retry-After.

Error response format

On failure the API returns standard HTTP status codes: 400 (client error with a JSON {"error": "message"}), 401 (unauthorized), 403 (forbidden), 412 (precondition failed), 429 (rate limited), 5xx (server error). Parse the JSON error message and HTTP status code to decide retry vs fix.

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