OpenStack Designate Python API Docs | dltHub

Designate is the OpenStack DNS service providing a REST API for managing DNS zones, recordsets, records, pools and related DNS resources. The REST API base URL is http://<designate-api-host>:9001/v2 and All requests require an OpenStack Keystone token (X-Auth-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 OpenStack Designate data in under 10 minutes.

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What data can I load from OpenStack Designate?

Here are some of the endpoints you can load from OpenStack Designate:

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How do I authenticate with the OpenStack Designate API?

Designate uses OpenStack Keystone for authentication. Obtain a scoped token from Keystone and include it in requests via the X-Auth-Token header (or Authorization: Bearer ). Admin-only headers X-Auth-All-Projects and X-Auth-Sudo-Project-ID are supported for cross-tenant operations.

1. Get your credentials

1. Log in to your OpenStack dashboard or use the OpenStack CLI. 2) Create or identify a user with appropriate role in the project/tenant that will access Designate. 3) Obtain a scoped token via Keystone (e.g., openstack token issue or POST to /v3/auth/tokens). 4) Use the returned token value in the X-Auth-Token header for Designate API requests.

2. Add them to .dlt/secrets.toml

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[sources.openstack_designate_source]
auth_token = "your_keystone_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 OpenStack Designate 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 openstack_designate_pipeline.py

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

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Pipeline openstack_designate_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset openstack_designate_data
The duckdb destination used duckdb:/openstack_designate.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 openstack_designate_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 zones and recordsets from the OpenStack Designate 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 openstack_designate_source(auth_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://<designate-api-host>:9001/v2",
            "auth": {
                "type": "bearer",
                "auth_token": auth_token,
            },
        },
        "resources": [
            {"name": "zones", "endpoint": {"path": "v2/zones", "data_selector": "zones"}},
            {"name": "recordsets", "endpoint": {"path": "v2/recordsets", "data_selector": "recordsets"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="openstack_designate_pipeline",
        destination="duckdb",
        dataset_name="openstack_designate_data",
    )
    load_info = pipeline.run(openstack_designate_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("openstack_designate_pipeline").dataset()
sessions_df = data.zones.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM openstack_designate_data.zones LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("openstack_designate_pipeline").dataset()
data.zones.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 OpenStack Designate 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 401/403 responses, verify your Keystone token is valid and scoped to the target project. Ensure X-Auth-Token is set, or use Authorization: Bearer if your environment expects it. Admin-only headers (X-Auth-All-Projects, X-Auth-Sudo-Project-ID) require appropriate admin roles.

Pagination and filtering

List endpoints use paginated responses. Use query parameters (limit, marker, etc.) as supported by the API. Inspect response headers for paging hints and next links if present.

Common HTTP errors

401 Unauthorized — missing/invalid Keystone token. 403 Forbidden — insufficient role/permissions or trying admin-only operation without admin role. 404 Not Found — resource not present. 409 Conflict — resource state conflict (e.g., duplicate). 413 Payload Too Large — body exceeds limits. 429 Too Many Requests — rate limiting if enforced by deployment.

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