Zonka Feedback Python API Docs | dltHub

Zonka Feedback is a customer feedback and experience management platform that exposes REST APIs to retrieve surveys, responses, contacts, devices, tasks, locations, users and related data. The REST API base URL is https://us1.apis.zonkafeedback.com and All requests require an admin Auth Token (API token) for authentication..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Zonka Feedback data in under 10 minutes.

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What data can I load from Zonka Feedback?

Here are some of the endpoints you can load from Zonka Feedback:

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How do I authenticate with the Zonka Feedback API?

Zonka uses an API token created by an admin in the Zonka UI. Include the token in the Authorization header as a Bearer token. All requests must be made over HTTPS.

1. Get your credentials

1. Sign in to Zonka Feedback as an Admin.
2. Navigate to Company Settings > Developers > API.
3. Open the APIs tab and click "Generate Token" to create the Auth Token.
4. Copy and securely store the token (generating a new token replaces the old one immediately).

2. Add them to .dlt/secrets.toml

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[sources.zonka_feedback_source]
api_token = "your_auth_token_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Zonka Feedback API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python zonka_feedback_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline zonka_feedback_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset zonka_feedback_data
The duckdb destination used duckdb:/zonka_feedback.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline zonka_feedback_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads responses and surveys from the Zonka Feedback API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def zonka_feedback_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://us1.apis.zonkafeedback.com",
            "auth": {
                "type": "bearer",
                "token": api_token,
            },
        },
        "resources": [
            {"name": "responses", "endpoint": {"path": "responses", "data_selector": "result"}},
            {"name": "surveys", "endpoint": {"path": "surveys", "data_selector": "result"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="zonka_feedback_pipeline",
        destination="duckdb",
        dataset_name="zonka_feedback_data",
    )
    load_info = pipeline.run(zonka_feedback_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("zonka_feedback_pipeline").dataset()
sessions_df = data.responses.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM zonka_feedback_data.responses LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("zonka_feedback_pipeline").dataset()
data.responses.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Zonka Feedback data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If you receive 401 Unauthorized, verify you are using an Admin‑generated Auth Token. Tokens are created at Company Settings > Developers > API. Generating a new token invalidates the old one immediately.

Pagination and selectors

Most list endpoints are paginated and return items under the result field (default page size 25). Use the page and page_size query parameters as documented for each endpoint.

Rate limits and server errors

The API returns standard HTTP codes: 200 OK, 400 Bad Request, 401 Unauthorized, 403 Forbidden, 404 Not Found, 500 Internal Server Error, 503 Temporary Server Error. On 5xx errors retry with exponential backoff and contact hello@zonkafeedback.com if persistent.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack