Load MailChimp data to DuckDB

Mailchimp Marketing API allows developers to manage audiences, sync data, and automate marketing activities. Everything needed to build a working MailChimp → 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 MailChimp 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 MailChimp 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 MailChimp 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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MailChimp API at a glance

These values come from the MailChimp API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the MailChimp API?

Authentication is performed using either HTTP Basic Authentication or Bearer Authentication. For Basic Auth, use 'anystring:API_KEY' as the user credentials; for Bearer Auth, include the 'Authorization: Bearer ' header.

1. Get your credentials

To obtain your Mailchimp API key, log in to your Mailchimp account. Once logged in, click your profile icon (often located in the top-right or bottom-left corner of the dashboard) and select Profile or Account & billing. Open the Extras dropdown menu in the navigation bar and select API keys. Scroll to the Your API Keys section and click the Create A Key button. Provide a descriptive name for the key so you can identify its purpose later, then click Generate Key. Copy the key immediately, as it will not be displayed again after you leave the page. Note that the API key includes a data center suffix (e.g., -us21) which is required for your API base URL.

2. Add them to .dlt/secrets.toml

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[sources.mailchimp_source]
api_key = "REPLACE_ME"

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 MailChimp data can I load into DuckDB?

These are the MailChimp endpoints dlt can load into DuckDB:

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How do I load only new MailChimp records?

MailChimp exposes since_date_created on 3.0/lists, 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": "lists", "endpoint": {
    "path": "3.0/lists",
    "data_selector": "lists",
    "incremental": {"cursor_path": "since_date_created", "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 MailChimp pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /lists and /lists/{list_id}/members from the MailChimp API into DuckDB:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def mailchimp_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{dc}.api.mailchimp.com/3.0/",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "lists", "endpoint": {"path": "3.0/lists", "data_selector": "lists"}},
            {"name": "list_members", "endpoint": {"path": "3.0/lists/{list_id}/members", "data_selector": "members"}}
        ],
    }
    yield from rest_api_resources(config)


def load_mailchimp_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="mailchimp_pipeline",
        destination="duckdb",
        dataset_name="mailchimp_data",
    )
    load_info = pipeline.run(mailchimp_source())
    print(load_info)


if __name__ == "__main__":
    load_mailchimp_to_duckdb()

Run it with python mailchimp_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 MailChimp 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("mailchimp_pipeline").dataset()
df = data.lists.df()
print(df.head())

SQL:

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SELECT * FROM mailchimp_data.lists LIMIT 10;

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

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How do I deploy the MailChimp 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 MailChimp 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 MailChimp 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