Load Kustomer data to DuckDB

Kustomer is an AI-powered customer service CRM that exposes REST APIs for managing resources like customers and conversations. Everything needed to build a working Kustomer → 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 Kustomer 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 Kustomer 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 Kustomer 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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Kustomer API at a glance

These values come from the Kustomer API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Kustomer API?

Kustomer uses Bearer token authentication. Requests must include an Authorization header with the value 'Bearer API_KEY'.

1. Get your credentials

1. Log in to your Kustomer organization as an administrator. 2. Navigate to Settings, then select Security, and finally click API Keys. 3. Click the Add API Key button. 4. Provide a descriptive name for the key. 5. Configure the desired permissions (Roles) for your integration and set the expiration duration. 6. Click Create to generate the key. 7. Copy the generated token immediately, as it cannot be retrieved again after leaving the confirmation screen.

2. Add them to .dlt/secrets.toml

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[sources.kustomer_source]
api_key = "your_kustomer_api_token_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 Kustomer data can I load into DuckDB?

These are the Kustomer endpoints dlt can load into DuckDB:

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How do I load only new Kustomer records?

Kustomer exposes updated_at on v1/customers, 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": "customers", "endpoint": {
    "path": "v1/customers",
    "data_selector": "data",
    "incremental": {"cursor_path": "updated_at", "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 Kustomer pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /v1/customers and /v1/conversations from the Kustomer API into DuckDB:

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

@dlt.source
def kustomer_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://orgname.api.kustomerapp.com",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "customers", "endpoint": {"path": "v1/customers", "data_selector": "data"}},
            {"name": "conversations", "endpoint": {"path": "v1/conversations", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_kustomer_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="kustomer_pipeline",
        destination="duckdb",
        dataset_name="kustomer_data",
    )
    load_info = pipeline.run(kustomer_source())
    print(load_info)


if __name__ == "__main__":
    load_kustomer_to_duckdb()

Run it with python kustomer_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 Kustomer 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("kustomer_pipeline").dataset()
df = data.customers.df()
print(df.head())

SQL:

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SELECT * FROM kustomer_data.customers LIMIT 10;

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

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How do I deploy the Kustomer 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 Kustomer 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 Kustomer 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