Zuora Python API Docs | dltHub

Zuora is a subscription management and billing platform that offers a REST API for managing accounts, subscriptions, orders, and custom objects. The REST API base URL is https://rest.zuora.com and All requests require a Bearer OAuth token 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 add "dlt[hub]" and start loading Zuora data in under 10 minutes.

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What data can I load from Zuora?

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

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How do I authenticate with the Zuora API?

Zuora uses OAuth 2.0. Obtain a bearer token from the /oauth/token endpoint and include it in each request as the Authorization header: "Bearer {access_token}".

1. Get your credentials

1. Log in to your Zuora tenant.
2. Navigate to Settings → Security → OAuth Clients.
3. Click Create New Client and fill in the required details (name, redirect URI, etc.).
4. After saving, note the generated client_id and client_secret.
5. To obtain an access token, make a POST request to https://rest.zuora.com/oauth/token with grant_type=client_credentials, client_id, and client_secret in the request body (application/x‑www‑form‑urlencoded).
6. The response will contain an access_token value to use for API calls.

2. Add them to .dlt/secrets.toml

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[sources.zuora_source]
access_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 init
uv add "dlt[hub]"

1. Install the dlt AI Workbench:

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uv run dlthub 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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uv run dlthub ai toolkit install rest-api-pipeline

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 Zuora 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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uv run python zuora_pipeline.py

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

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Pipeline zuora_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset zuora_data
The duckdb destination used duckdb:/zuora.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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uv run dlthub 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 accounts and subscriptions from the Zuora 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 zuora_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://rest.zuora.com",
            "auth": {
                "type": "bearer",
                "access_token": access_token,
            },
        },
        "resources": [
            {"name": "accounts", "endpoint": {"path": "v1/accounts"}},
            {"name": "subscriptions", "endpoint": {"path": "v1/subscriptions"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="zuora_pipeline",
        destination="duckdb",
        dataset_name="zuora_data",
    )
    load_info = pipeline.run(zuora_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("zuora_pipeline").dataset()
sessions_df = data.accounts.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM zuora_data.accounts LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("zuora_pipeline").dataset()
data.accounts.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 Zuora 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 bearer token is missing, expired, or invalid. Ensure you are requesting a fresh token from /oauth/token and that the Authorization: Bearer <token> header is correctly set.

Rate Limiting

• 429 Too Many Requests – Zuora enforces request throttling. If you receive this response, back off for a few seconds and retry. Consider implementing exponential backoff.

Pagination

• Zuora GET list endpoints support page and size query parameters. Example: GET /v1/orders?page=1&size=200. Continue fetching pages until the response contains no further records.

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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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-runtime

• 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