Load Kong OpenID Connect data to DuckDB

Kong OpenID Connect is a plugin that integrates Kong Gateway with third-party identity providers to handle authentication and authorization flows. Everything needed to build a working Kong OpenID Connect → 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 Kong OpenID Connect 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 Kong OpenID Connect 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 Kong OpenID Connect 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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Kong OpenID Connect API at a glance

These values come from the Kong OpenID Connect API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Kong OpenID Connect API?

The Kong Admin API requires authentication via a Bearer token in the 'Authorization' header when communicating with secure control planes, or is accessed via local ports 8001/8444 for administrative management.

1. Get your credentials

The Kong OpenID Connect plugin does not use plugin-specific credentials. It is managed via the Kong Admin API, typically accessible at http://{KONG_ADMIN_HOST}:8001. In production, you must secure this Admin API using Kong's built-in authentication (such as RBAC or ACLs) rather than relying on default open access. To authenticate the dlt pipeline, you should provide the appropriate Admin API credentials via the configuration.

2. Add them to .dlt/secrets.toml

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[sources.kong_openid_connect_source]
admin_url = "http://your-admin-host:8001"
# If the Admin API is protected, include credentials here:
# api_key = "your_admin_api_key"

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 Kong OpenID Connect data can I load into DuckDB?

These are the Kong OpenID Connect endpoints dlt can load into DuckDB:

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How do I load only new Kong OpenID Connect records?

Kong OpenID Connect exposes offset on openid-connect/issuers, 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": "openid_connect_issuers", "endpoint": {
    "path": "openid-connect/issuers",
    "data_selector": "data",
    "incremental": {"cursor_path": "offset", "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 Kong OpenID Connect pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading openid-connect/jwks and openid-connect/discovery-cache from the Kong OpenID Connect API into DuckDB:

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

@dlt.source
def kong_openid_connect_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://localhost:8001",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "openid_connect_issuers", "endpoint": {"path": "openid-connect/issuers", "data_selector": "data"}},
            {"name": "plugins", "endpoint": {"path": "plugins", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_kong_openid_connect_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="kong_openid_connect_pipeline",
        destination="duckdb",
        dataset_name="kong_openid_connect_data",
    )
    load_info = pipeline.run(kong_openid_connect_source())
    print(load_info)


if __name__ == "__main__":
    load_kong_openid_connect_to_duckdb()

Run it with python kong_openid_connect_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 Kong OpenID Connect 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("kong_openid_connect_pipeline").dataset()
df = data.openid_connect_issuers.df()
print(df.head())

SQL:

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SELECT * FROM kong_openid_connect_data.openid_connect_issuers LIMIT 10;

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

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How do I deploy the Kong OpenID Connect 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 Kong OpenID Connect 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 Kong OpenID Connect 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