Confluent Cloud Python API Docs | dltHub

Confluent Cloud is a fully managed, cloud‑native Apache Kafka service with REST APIs for Kafka, Schema Registry, and Stream Catalog. The REST API base URL is https://{cluster}.region.provider.confluent.cloud and All requests require either HTTP Basic (API key/secret) or Bearer token 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 Confluent Cloud data in under 10 minutes.

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What data can I load from Confluent Cloud?

Here are some of the endpoints you can load from Confluent Cloud:

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How do I authenticate with the Confluent Cloud API?

Confluent Cloud authenticates requests via HTTP Basic (API key and secret) or Bearer tokens; the Authorization header must contain either Basic <base64(key:secret)> or Bearer .

1. Get your credentials

1. Log in to Confluent Cloud console.
2. Select the desired environment and cluster.
3. In the left navigation, click API Keys.
4. Click Create key and choose the service (Kafka, Schema Registry, etc.).
5. Copy the generated API key and API secret; store them securely.
6. (Optional) For OAuth, create a token via the Security Token Service as described in the docs.

2. Add them to .dlt/secrets.toml

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[sources.confluent_cloud_source]
api_key = "your_api_key_here"
api_secret = "your_api_secret_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 Confluent Cloud 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 confluent_cloud_pipeline.py

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

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Pipeline confluent_cloud_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset confluent_cloud_data
The duckdb destination used duckdb:/confluent_cloud.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 confluent_cloud_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 subjects and schemas from the Confluent Cloud 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 confluent_cloud_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{cluster}.region.provider.confluent.cloud",
            "auth": {
                "type": "http_basic",
                "api_secret": api_key,
            },
        },
        "resources": [
            {"name": "subjects", "endpoint": {"path": "subjects"}},
            {"name": "schemas", "endpoint": {"path": "schemas"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="confluent_cloud_pipeline",
        destination="duckdb",
        dataset_name="confluent_cloud_data",
    )
    load_info = pipeline.run(confluent_cloud_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("confluent_cloud_pipeline").dataset()
sessions_df = data.subjects.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM confluent_cloud_data.subjects LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("confluent_cloud_pipeline").dataset()
data.subjects.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 Confluent Cloud 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 Errors

• 401 Unauthorized – Invalid API key/secret or missing Authorization header.
• 403 Forbidden – Credentials are valid but lack required permissions.

Rate Limiting

• 429 Too Many Requests – The client has exceeded the allowed request quota. Back‑off and retry after the Retry-After header interval.

Pagination Quirks

• Endpoints that support pagination return offset and limit query parameters. Missing or out‑of‑range values may result in 400 Bad Request. Ensure limit is within the allowed maximum (often 1000) and increment offset based on the number of records returned.

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