Load Pendo Engage data to DuckDB

Pendo Engage is a REST API that provides programmatic access to product analytics data and in-app guide management for Pendo subscriptions. Everything needed to build a working Pendo Engage → 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 Pendo Engage 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 Pendo Engage 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 Pendo Engage 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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Pendo Engage API at a glance

These values come from the Pendo Engage API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Pendo Engage API?

Authentication is performed by passing a Pendo integration key in the HTTP request header 'x-pendo-integration-key'.

1. Get your credentials

1. Log in to your Pendo Engage account as an Admin. 2. Navigate to Settings in the top-right menu. 3. Select Integrations, then choose Integration Keys. 4. Click + Add Integration Key. 5. Provide a description, and ensure you select the appropriate access permissions (e.g., Allow Write Access if required for your pipeline). 6. Save the key immediately; the full key value is only displayed once at the time of creation and cannot be retrieved later.

2. Add them to .dlt/secrets.toml

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[sources.pendo_engage_source]
pendo_integration_key = "your_integration_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 Pendo Engage data can I load into DuckDB?

These are the Pendo Engage endpoints dlt can load into DuckDB:

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How do I load only new Pendo Engage records?

The Pendo Engage 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": "aggregation", "endpoint": {
    "path": "api/v1/aggregation",
    # 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 Pendo Engage pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /api/v1/aggregation and /api/v1/visitor from the Pendo Engage API into DuckDB:

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

@dlt.source
def pendo_engage_source(integration_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://app.pendo.io",
            "auth": {"type": "api_key", "api_key": integration_key, "name": "x-pendo-integration-key", "location": "header"},
        },
        "resources": [
            {"name": "aggregation", "endpoint": {"path": "api/v1/aggregation", "data_selector": "results"}},
            {"name": "visitor_query", "endpoint": {"path": "api/v1/aggregation", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def load_pendo_engage_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="pendo_engage_pipeline",
        destination="duckdb",
        dataset_name="pendo_engage_data",
    )
    load_info = pipeline.run(pendo_engage_source())
    print(load_info)


if __name__ == "__main__":
    load_pendo_engage_to_duckdb()

Run it with python pendo_engage_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 Pendo Engage 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("pendo_engage_pipeline").dataset()
df = data.aggregation.df()
print(df.head())

SQL:

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SELECT * FROM pendo_engage_data.aggregation LIMIT 10;

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

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How do I deploy the Pendo Engage 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 Pendo Engage 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 Pendo Engage 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