Bigmarker Python API Docs | dltHub

BigMarker is a web‑based platform for hosting live webinars, virtual events, and online meetings. The REST API base URL is https://www.bigmarker.com/api/v1 and All requests require an API‑KEY header for authentication..

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 Bigmarker data in under 10 minutes.

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What data can I load from Bigmarker?

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

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How do I authenticate with the Bigmarker API?

Authentication is performed by sending the API‑KEY value in the HTTP header API-KEY. The key is generated in the API Keys section of the BigMarker account settings.

1. Get your credentials

1. Log in to your BigMarker account.
2. Navigate to Settings → API Keys (or open https://www.bigmarker.com/settings/api_keys).
3. Click “Create New API Key” (or “Request API Key”).
4. Copy the generated API‑KEY and note the Base URL shown on the same page.
5. Store the key securely for use in API calls.

2. Add them to .dlt/secrets.toml

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[sources.bigmarker_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 Bigmarker 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 bigmarker_pipeline.py

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

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Pipeline bigmarker_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset bigmarker_data
The duckdb destination used duckdb:/bigmarker.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 bigmarker_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 conferences and sessions from the Bigmarker 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 bigmarker_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.bigmarker.com/api/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "conferences", "endpoint": {"path": "conferences/", "data_selector": "conferences"}},
            {"name": "sessions", "endpoint": {"path": "sessions/", "data_selector": "sessions"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="bigmarker_pipeline",
        destination="duckdb",
        dataset_name="bigmarker_data",
    )
    load_info = pipeline.run(bigmarker_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("bigmarker_pipeline").dataset()
sessions_df = data.conferences.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM bigmarker_data.conferences LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("bigmarker_pipeline").dataset()
data.conferences.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 Bigmarker 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 Errors

• 401 Unauthorized – Occurs when the API-KEY header is missing or invalid.
• 403 Forbidden – The API key does not have permission to access the requested resource.

Request Errors

• 400 Bad Request – Invalid request parameters.
• 406 Not Acceptable – Requested format not supported.

Server Errors

• 500 Internal Server Error – Unexpected server condition.
• 503 Service Unavailable – Service temporarily unavailable.

Pagination

Responses include pagination fields such as current_page, per_page, total_pages, and total_entries. Use these to iterate through all pages of results.

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