Bizzabo Python API Docs | dltHub

Bizzabo is an event management platform exposing event, registration, attendee, agenda and related data via a REST API. The REST API base URL is https://api.bizzabo.com/api and Requests require an API key provided in the Authorization header (some partner endpoints use OAuth2)..

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 Bizzabo data in under 10 minutes.

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What data can I load from Bizzabo?

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

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How do I authenticate with the Bizzabo API?

Provide your API key in the Authorization header for requests (e.g., Authorization: <api_key>). Partner APIs may require OAuth2 access tokens.

1. Get your credentials

1. Sign in to your Bizzabo account (or partner dashboard). 2. Navigate to Developer / API Keys or Integrations. 3. Create a new API key or app and copy the generated key. 4. If using partner OAuth2, register your app to obtain client_id and client_secret and follow OAuth2 flows to obtain tokens.

2. Add them to .dlt/secrets.toml

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[sources.bizzabo_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 Bizzabo 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 bizzabo_pipeline.py

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

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Pipeline bizzabo_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset bizzabo_data
The duckdb destination used duckdb:/bizzabo.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 bizzabo_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 registrations and events from the Bizzabo 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 bizzabo_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.bizzabo.com/api",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "registrations", "endpoint": {"path": "registrations", "data_selector": "content"}},
            {"name": "events", "endpoint": {"path": "events", "data_selector": "content"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="bizzabo_pipeline",
        destination="duckdb",
        dataset_name="bizzabo_data",
    )
    load_info = pipeline.run(bizzabo_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("bizzabo_pipeline").dataset()
sessions_df = data.registrations.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM bizzabo_data.registrations LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("bizzabo_pipeline").dataset()
data.registrations.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 Bizzabo 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 requests return 401/403 ensure the API key is provided in the Authorization header exactly as shown in examples. Partner endpoints may require OAuth2 access tokens.

Rate limits and throttling

The docs indicate standard API response codes for throttling; if you receive 429 handle with exponential backoff.

Pagination quirks

List endpoints use page and size query params (size max 200). Responses contain a top-level 'content' array, 'page' metadata (size, totalElements, totalPages, number) and 'links' array (rel: first/self/next/last/prev). Use the 'page' params or follow 'links.next' href.

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