Load Confluent data to DuckDB

Confluent Cloud and REST Proxy APIs provide interfaces for managing resources, producing and consuming Kafka records, and configuring cluster components. Everything needed to build a working Confluent → 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 Confluent 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 Confluent 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 Confluent 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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Confluent API at a glance

These values come from the Confluent API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Confluent API?

API requests generally require an Authorization header using either 'Basic' authentication (API Key ID and Secret base64-encoded) or 'Bearer' authentication (for OAuth tokens/STS). Basic auth credentials must be colon-separated (key:secret) and base64-encoded.

1. Get your credentials

1. Log in to the Confluent Cloud Console (https://confluent.cloud). 2. Navigate to your desired Kafka cluster or service account profile. 3. Go to the 'API keys' section or tab. 4. Click 'Add API key' (or 'Create key'). 5. Follow the prompts to select the resource scope (e.g., 'Global' or a specific Kafka cluster). 6. Copy the generated 'API key' and 'API secret' immediately, as the secret cannot be retrieved later. 7. Store these credentials securely.

2. Add them to .dlt/secrets.toml

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[sources.confluent_source]
api_key = "your_api_key_here"
api_secret = "your_api_secret_here"

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 Confluent data can I load into DuckDB?

These are the Confluent endpoints dlt can load into DuckDB:

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How do I load only new Confluent records?

Confluent exposes page_token on cmk/v2/clusters, 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": "kafka_clusters", "endpoint": {
    "path": "cmk/v2/clusters",
    "data_selector": "data",
    "incremental": {"cursor_path": "page_token", "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 Confluent pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /topics and /clusters from the Confluent API into DuckDB:

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

@dlt.source
def confluent_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.confluent.cloud (for Confluent Cloud platform APIs) or specific cluster-provided URLs for Kafka REST APIs (e.g., https://pkc-abcde.us-west4.gcp.confluent.cloud)",
            "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": api_key},
        },
        "resources": [
            {"name": "kafka_clusters", "endpoint": {"path": "cmk/v2/clusters", "data_selector": "data"}},
            {"name": "kafka_topics", "endpoint": {"path": "kafka/v3/clusters/{cluster_id}/topics", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_confluent_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="confluent_pipeline",
        destination="duckdb",
        dataset_name="confluent_data",
    )
    load_info = pipeline.run(confluent_source())
    print(load_info)


if __name__ == "__main__":
    load_confluent_to_duckdb()

Run it with python confluent_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 Confluent 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("confluent_pipeline").dataset()
df = data.kafka_clusters.df()
print(df.head())

SQL:

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SELECT * FROM confluent_data.kafka_clusters LIMIT 10;

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

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How do I deploy the Confluent 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 Confluent 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 Confluent 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