Zendesk - Support Python API Docs | dltHub

Zendesk Support is a customer service platform that provides ticketing, user and organization management via a REST API. The REST API base URL is https://{subdomain}.zendesk.com/api/v2 and All requests require authentication: either HTTP Basic with email+API token or OAuth2 bearer token..

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 Zendesk - Support data in under 10 minutes.

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What data can I load from Zendesk - Support?

Here are some of the endpoints you can load from Zendesk - Support:

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How do I authenticate with the Zendesk - Support API?

Zendesk Support API supports HTTP Basic authentication using an agent's email and API token (email/token as username and token as password) or OAuth 2.0 bearer tokens. Requests must include the Accept: application/json header.

1. Get your credentials

1. Sign in to your Zendesk account as an admin.
2. Navigate to Admin Center → Channels → API (or Settings → API).
3. In the Token Access section, enable token access if it is disabled.
4. Click "Add API token" to generate a new token and copy it (it is shown only once).
5. Use agent_email/token as the username and the API token as the password for Basic auth, or register an OAuth client to obtain a bearer token.

2. Add them to .dlt/secrets.toml

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[sources.zendesk_support_source]
email = "agent@example.com"
api_token = "your_api_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 Zendesk - Support 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 zendesk_support_pipeline.py

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

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Pipeline zendesk_support_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset zendesk_support_data
The duckdb destination used duckdb:/zendesk_support.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 zendesk_support_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 tickets and users from the Zendesk - Support 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 zendesk_support_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{subdomain}.zendesk.com/api/v2",
            "auth": {
                "type": "http_basic",
                "api_token": api_token,
            },
        },
        "resources": [
            {"name": "tickets", "endpoint": {"path": "api/v2/tickets", "data_selector": "tickets"}},
            {"name": "users", "endpoint": {"path": "api/v2/users", "data_selector": "users"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="zendesk_support_pipeline",
        destination="duckdb",
        dataset_name="zendesk_support_data",
    )
    load_info = pipeline.run(zendesk_support_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("zendesk_support_pipeline").dataset()
sessions_df = data.tickets.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM zendesk_support_data.tickets LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("zendesk_support_pipeline").dataset()
data.tickets.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 Zendesk - Support 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

Zendesk returns 401 Unauthorized for invalid credentials. For API‑token Basic auth, ensure the username is "agent_email/token" and the password is the token; otherwise use a valid OAuth bearer token.

Rate limits and 429 responses

Many endpoints enforce rate limits. Exceeding the quota returns 429 Too Many Requests. Implement exponential backoff and respect the Retry‑After header when present.

Pagination quirks

Zendesk supports offset (page/per_page) and cursor‑based pagination (page[size], page[after]/page[before]). List endpoints return next_page and previous_page URLs and a top‑level array (e.g., tickets, users). Incremental exports include end_of_stream or after cursors. Do not assume a top‑level array; use the documented data selector keys.

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