Zoom Python API Docs | dltHub

Zoom is a cloud communications platform providing video conferencing, online meetings, chat, phone, webinar, and contact-center APIs. The REST API base URL is https://api.zoom.us/v2/ and All API requests require an OAuth 2.0 Bearer access token (server-to-server OAuth or user OAuth)..

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 Zoom data in under 10 minutes.

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What data can I load from Zoom?

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

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How do I authenticate with the Zoom API?

Obtain an access token via OAuth 2.0 (authorization code flow) or server-to-server OAuth (account-level). Include the token in the Authorization header as: Authorization: Bearer <access_token>. Use Content-Type: application/json for JSON requests.

1. Get your credentials

1. Sign in to Zoom Marketplace (marketplace.zoom.us). 2) Create an app (OAuth or Server-to-Server OAuth). 3) For OAuth: configure redirect URI, obtain client_id and client_secret, run authorization code flow to exchange code for access_token. For server-to-server: create Server-to-Server OAuth app and use client_id/client_secret to request an access token from https://zoom.us/oauth/token with grant_type=account_credentials. 4) Save the returned access_token (valid ~1 hour); refresh or request a new token when expired.

2. Add them to .dlt/secrets.toml

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[sources.zoom_source]
token = "your_access_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 Zoom 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 zoom_pipeline.py

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

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Pipeline zoom_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset zoom_data
The duckdb destination used duckdb:/zoom.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 zoom_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 users and meetings from the Zoom 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 zoom_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.zoom.us/v2/",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "users", "data_selector": "users"}},
            {"name": "meetings", "endpoint": {"path": "users/{userId}/meetings", "data_selector": "meetings"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="zoom_pipeline",
        destination="duckdb",
        dataset_name="zoom_data",
    )
    load_info = pipeline.run(zoom_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("zoom_pipeline").dataset()
sessions_df = data.users.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM zoom_data.users LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("zoom_pipeline").dataset()
data.users.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 Zoom 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 Unauthorized: verify your Bearer token is valid and not expired. For OAuth apps ensure the access token was obtained correctly using client_id/client_secret and correct grant_type; for server-to-server OAuth ensure account-level scopes are granted.

Permission / scope errors

403 Forbidden occurs when the app lacks required scopes (for example user:read:admin for account-level user listing). Update app scopes in Zoom Marketplace and reinstall the app.

Rate limits (429)

Zoom enforces QPS and daily limits. When you hit limits you receive HTTP 429 with retry headers and/or Retry-After. Implement exponential backoff and respect X-RateLimit-* headers; use smaller page_size and reduce concurrency.

Pagination quirks

Many list endpoints return page_count, page_number, page_size, total_records and next_page_token. Use next_page_token to fetch subsequent pages (pass as query param next_page_token). If next_page_token is empty there are no more pages. Large page_size values may be ignored; use recommended defaults.

Common error bodies

Errors typically include JSON like {"code": , "message": ""}. 401 for invalid token, 403 for insufficient scope, 429 for rate limit, 404 for not found.

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