Confluent Cloud Python API Docs | dltHub

Confluent Cloud REST API is the management plane for Confluent Cloud used to manage organizations, environments, clusters, service accounts, and API keys. The REST API base URL is https://api.confluent.cloud and requests require HTTP Basic authentication using an API key ID and API secret.

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 add "dlt[hub]" 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 REST API supports HTTP Basic authentication where the API key ID and API secret are joined by a colon (ID

) and base64-encoded. This encoded string must be provided in the 'Authorization' header as 'Basic {encoded_credentials}'.

1. Get your credentials

1. Log in to the Confluent Cloud Console. 2. Navigate to your specific Kafka cluster or global settings, depending on the required scope. 3. Select 'Cluster Settings' or 'API Keys' from the sidebar menu. 4. Click 'Create key' to generate a new API key pair. 5. Download or copy the API Key and API Secret immediately, as the secret is not retrievable once the page is closed. 6. Combine the key and secret as a string in the format 'api_key
   '. 7. Base64-encode this string to create the credential value for HTTP Basic Authentication.

2. Add them to .dlt/secrets.toml

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[sources.confluent_cloud_source]
api_key = "your_api_key_id"
api_secret = "your_api_secret"
# Alternatively, if using the base64-encoded string directly for raw requests:
# auth_header = "Basic <base64_encoded_string>"

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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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uv run dlthub 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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uv run dlthub ai toolkit install rest-api-pipeline

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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uv run 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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uv run dlthub 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 /kafka/v3 and /iam/v2/api-keys 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://api.confluent.cloud",
            "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": api_key},
        },
        "resources": [
            {"name": "clusters", "endpoint": {"path": "cmk/v2/clusters", "data_selector": "data"}},
            {"name": "networks", "endpoint": {"path": "networking/v1/networks", "data_selector": "data"}}
        ],
    }
    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.clusters.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM confluent_cloud_data.clusters LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("confluent_cloud_pipeline").dataset()
data.clusters.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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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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