Benzinga Python API Docs | dltHub

Benzinga is a financial data platform providing market data, news, and company information via REST APIs. The REST API base URL is https://api.benzinga.com and All requests require an API key supplied as the query parameter named 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 Benzinga data in under 10 minutes.

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What data can I load from Benzinga?

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

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How do I authenticate with the Benzinga API?

Benzinga uses an ApiKey scheme where the token must be provided in the query string parameter named "token" for protected endpoints.

1. Get your credentials

1. Sign in or register at the Benzinga developer portal (https://docs.benzinga.com/ or the provider dashboard). 2) Navigate to API Keys / Credentials in your account settings. 3) Create or copy an API token for Data API access. 4) Store the token securely and pass it as the token query parameter in requests.

2. Add them to .dlt/secrets.toml

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[sources.benzinga_bars_source]
api_key = "your_benzinga_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 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 Benzinga 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 benzinga_bars_pipeline.py

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

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Pipeline benzinga_bars_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset benzinga_bars_data
The duckdb destination used duckdb:/benzinga_bars.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 benzinga_bars_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 bars and news from the Benzinga 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 benzinga_bars_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.benzinga.com",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "bars", "endpoint": {"path": "api/v2/bars"}},
            {"name": "news", "endpoint": {"path": "api/v2/news", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="benzinga_bars_pipeline",
        destination="duckdb",
        dataset_name="benzinga_bars_data",
    )
    load_info = pipeline.run(benzinga_bars_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("benzinga_bars_pipeline").dataset()
sessions_df = data.bars.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM benzinga_bars_data.bars LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("benzinga_bars_pipeline").dataset()
data.bars.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 Benzinga 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 401/403 or an auth_failed error code, verify you supplied a valid token as the query parameter named token. Ensure the token is not expired and copied exactly; for example: ?token=YOUR_TOKEN.

Rate limits and pagination

Benzinga enforces rate limits based on your subscription plan. Pagination for some endpoints uses page/limit or offset‑style parameters; large result sets (e.g., news, calendar) may be capped (pagination limits referenced in dltHub notes: offsets limited and some APIs cannot return >10,000 items). For Bars, the response is returned as aggregated arrays per‑symbol and may require time‑range slicing to fetch large historical ranges.

Common error responses

Benzinga uses a standard response wrapper for errors with shape { "ok": false, "errors": [{ "code":"<error_code>","id":"<id>","value":"<message>" }], "data": {...} }. Common error codes include: bad_request, auth_failed, no_data_found, internal_server_error, upstream_api_error. HTTP 400 is returned for missing/invalid parameters; HTTP 500 for internal errors.

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