Kubernetes API Reference Python API Docs | dltHub

Kubernetes API is the REST API served by a Kubernetes cluster to manage and inspect cluster resources. The REST API base URL is Cluster-specific API base URLs (examples): - Core group: https://<kubernetes-api-server-host>/api/v1 - Named groups: https://<kubernetes-api-server-host>/apis/<group>/<version> - Discovery: https://<kubernetes-api-server-host>/api and https://<kubernetes-api-server-host>/apis - OpenAPI: https://<kubernetes-api-server-host>/openapi/v3 and All requests require cluster-authenticated credentials (e.g., Bearer token or client certificate)..

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 Kubernetes API Reference data in under 10 minutes.

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What data can I load from Kubernetes API Reference?

Here are some of the endpoints you can load from Kubernetes API Reference:

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How do I authenticate with the Kubernetes API Reference API?

Kubernetes API accepts client authentication via TLS client certificates, bearer tokens (Authorization: Bearer ), and other pluggable mechanisms. Typical client setups use a kubeconfig file with a token or client-certificate and CA; include Authorization and/or present TLS client certs when connecting to the API server.

1. Get your credentials

1. Obtain kubeconfig access from cluster administrator or cloud provider (e.g., EKS/GKE/AKS) which contains user credentials.
2. For a service account token: create a ServiceAccount and bind appropriate RBAC; then read the token from the secret mounted for that service account (or use kubectl -n get secret -o jsonpath='{.data.token}' | base64 -d).
3. For user tokens: use cloud provider IAM to create or retrieve kubeconfig credentials (e.g., gcloud, aws eks update-kubeconfig).
4. Use the token or client-certificate/CA from kubeconfig when configuring dlt source.

2. Add them to .dlt/secrets.toml

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[sources.kubernetes_api_reference_source]
token = "your_service_account_token_here"
api_server = "https://your.k8s.api.server:6443"
ca_cert_path = "/path/to/ca.crt"  # optional if using TLS verification

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 Kubernetes API Reference 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 kubernetes_api_reference_pipeline.py

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

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Pipeline kubernetes_api_reference_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset kubernetes_api_reference_data
The duckdb destination used duckdb:/kubernetes_api_reference.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 kubernetes_api_reference_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 pods and services from the Kubernetes API Reference 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 kubernetes_api_reference_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "Cluster-specific API base URLs (examples): - Core group: https://<kubernetes-api-server-host>/api/v1 - Named groups: https://<kubernetes-api-server-host>/apis/<group>/<version> - Discovery: https://<kubernetes-api-server-host>/api and https://<kubernetes-api-server-host>/apis - OpenAPI: https://<kubernetes-api-server-host>/openapi/v3",
            "auth": {
                "type": "bearer",
                "token": token,
            },
        },
        "resources": [
            {"name": "pods", "endpoint": {"path": "api/v1/namespaces/{namespace}/pods", "data_selector": "items"}},
            {"name": "services", "endpoint": {"path": "api/v1/namespaces/{namespace}/services", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="kubernetes_api_reference_pipeline",
        destination="duckdb",
        dataset_name="kubernetes_api_reference_data",
    )
    load_info = pipeline.run(kubernetes_api_reference_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("kubernetes_api_reference_pipeline").dataset()
sessions_df = data.pods.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM kubernetes_api_reference_data.pods LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("kubernetes_api_reference_pipeline").dataset()
data.pods.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 Kubernetes API Reference 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 or 403 Forbidden, verify the token or client certificate used. Ensure the token is valid, not expired, and the Kubernetes RBAC grants the requested read permissions to the target resource and namespace.

Rate limits and throttling

Kubernetes API server may enforce rate limiting on clients; 429 Too Many Requests can be returned. Implement exponential backoff and retries for 429 and 5xx errors.

Pagination

Some list endpoints support limit and continue query parameters for pagination. Use ?limit= and the returned metadata.continue value to page through lists until continue is empty.

Discovery and versioning quirks

API paths vary by API group/version; use /api and /apis discovery endpoints or the cluster OpenAPI (/openapi/v3) to enumerate available group versions and resource paths for the target cluster.

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