Load Unbounce data to DuckDB

Unbounce is a landing page builder and conversion platform that provides a REST API for managing accounts, sub-accounts, domains, page groups, pages, and leads. Everything needed to build a working Unbounce → 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 Unbounce 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 Unbounce 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 Unbounce 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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Unbounce API at a glance

These values come from the Unbounce API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Unbounce API?

Authentication is performed using either HTTP Basic Auth with the API Key as the username and an empty password, or an OAuth 2.0 Bearer token. All requests must include the 'Accept: application/vnd.unbounce.api.v0.4+json' header.

1. Get your credentials

1. Log in to your Unbounce account at https://app.unbounce.com. 2. Click your profile icon in the top-right corner. 3. Navigate to 'Account Settings' or 'Account Overview'. 4. Select 'API Access' from the left-hand sidebar menu. 5. Click 'Create New API Key'. 6. Copy the key immediately; it will not be displayed again. If 'API Access' is not visible, you may need to request API access via the Unbounce support portal.

2. Add them to .dlt/secrets.toml

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[sources.unbounce_source]
api_key = "your_unbounce_api_key_here"

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 Unbounce data can I load into DuckDB?

These are the Unbounce endpoints dlt can load into DuckDB:

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How do I load only new Unbounce records?

The Unbounce API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "pages", "endpoint": {
    "path": "pages",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "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 Unbounce pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading accounts and pages from the Unbounce API into DuckDB:

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

@dlt.source
def unbounce_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.unbounce.com",
            "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": api_key},
        },
        "resources": [
            {"name": "pages", "endpoint": {"path": "pages", "data_selector": "pages"}},
            {"name": "page_leads", "endpoint": {"path": "pages/{page_id}/leads", "data_selector": "leads"}}
        ],
    }
    yield from rest_api_resources(config)


def load_unbounce_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="unbounce_pipeline",
        destination="duckdb",
        dataset_name="unbounce_data",
    )
    load_info = pipeline.run(unbounce_source())
    print(load_info)


if __name__ == "__main__":
    load_unbounce_to_duckdb()

Run it with python unbounce_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 Unbounce 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("unbounce_pipeline").dataset()
df = data.pages.df()
print(df.head())

SQL:

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SELECT * FROM unbounce_data.pages LIMIT 10;

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

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How do I deploy the Unbounce 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 Unbounce 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 Unbounce 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