Load Zuora data to DuckDB

Zuora REST API provides access to billing, commerce, and finance operations including account management and report generation. Everything needed to build a working Zuora → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Zuora to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Zuora to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Zuora API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Zuora API at a glance

These values come from the Zuora API documentation. Check them against the vendor's current reference before relying on them in production.

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How do I authenticate with the Zuora API?

The API uses OAuth v2.0 bearer tokens, which must be passed in an 'Authorization' HTTP header using the format 'Bearer {token}'.

1. Get your credentials

To obtain your Zuora REST API credentials, follow these steps: 1. Log in to the Zuora UI. 2. Navigate to Settings > Administration > Manage Users. 3. Locate the user you wish to use for API access (ensure the user is 'Active' and has a Platform role with 'API Write Access' permission). 4. In the 'New OAuth Clients' section, enter a name for the client and click 'Create'. 5. Copy the displayed 'Client ID' and 'Client Secret' immediately, as the secret will not be visible again. If you lack administrator privileges, request that your company’s Zuora administrator generate these credentials for you.

2. Add them to .dlt/secrets.toml

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[sources.zuora_source]
zuora_client_id = "your_client_id_here"
zuora_client_secret = "your_client_secret_here"
zuora_base_url = "https://rest.zuora.com" # Or the appropriate REST endpoint for your tenant, e.g., https://rest.apisandbox.zuora.com

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Zuora data can I load into DuckDB?

These are the Zuora endpoints dlt can load into DuckDB:

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How do I load only new Zuora records?

Zuora exposes updatedDate on accounts, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "accounts", "endpoint": {
    "path": "accounts",
    "data_selector": "data",
    "incremental": {"cursor_path": "updatedDate", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Zuora pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /oauth/token (to generate a bearer token) and /v1/action/query or other resource-specific endpoints (for subsequent API data operations). from the Zuora API into DuckDB:

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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 (or environment-specific variations like https://rest.test.zuora.com)",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "accounts", "endpoint": {"path": "accounts", "data_selector": "data"}},
            {"name": "object_query_accounts", "endpoint": {"path": "object-query/accounts"}}
        ],
    }
    yield from rest_api_resources(config)


def load_zuora_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="zuora_pipeline",
        destination="duckdb",
        dataset_name="zuora_data",
    )
    load_info = pipeline.run(zuora_source())
    print(load_info)


if __name__ == "__main__":
    load_zuora_to_duckdb()

Run it with python zuora_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Zuora data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("zuora_pipeline").dataset()
df = data.accounts.df()
print(df.head())

SQL:

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SELECT * FROM zuora_data.accounts LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Zuora to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Zuora loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Zuora data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack