Confluent Cloud Telemetry Python API Docs | dltHub

Confluent Cloud Telemetry (Metrics) REST API provides access to metrics data for Confluent Cloud resources. The REST API base URL is https://api.telemetry.confluent.cloud and all requests require either Basic authentication with an API key/secret or an OAuth 2.0 Bearer token.

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 Telemetry data in under 10 minutes.

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What data can I load from Confluent Cloud Telemetry?

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

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How do I authenticate with the Confluent Cloud Telemetry API?

Authentication is performed using Basic HTTP authentication, where the API Key serves as the username and the API Secret serves as the password, provided in the 'Authorization: Basic' header. Alternatively, OAuth 2.0 is supported by passing a Confluent Security Token Service (STS) access token in the 'Authorization: Bearer' header.

1. Get your credentials

1. Log in to the Confluent Cloud Console (https://confluent.cloud). 2. Navigate to the Administration menu (☰) in the top-right corner and select 'API keys'. 3. Click 'Add API key'. 4. Select the 'My account' or 'Service account' tile. 5. Choose 'Cloud resource management' as the scope for the key (this ensures it is a 'Cloud API Key' capable of accessing the Telemetry/Metrics API). 6. Name your key and provide a description, then click 'Create API key'. 7. Download or copy the generated API key and secret immediately, as the secret cannot be retrieved later.

2. Add them to .dlt/secrets.toml

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[sources.confluent_cloud_telemetry_source]
confluent_api_key = "your_api_key_here"
confluent_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 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 Telemetry 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_telemetry_pipeline.py

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

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Pipeline confluent_cloud_telemetry_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset confluent_cloud_telemetry_data
The duckdb destination used duckdb:/confluent_cloud_telemetry.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 /v2/metrics/cloud/descriptors/resources and /v2/metrics/cloud/descriptors/metrics from the Confluent Cloud Telemetry 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_telemetry_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.telemetry.confluent.cloud",
            "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": api_key},
        },
        "resources": [
            {"name": "query_metrics", "endpoint": {"path": "query", "data_selector": "data"}},
            {"name": "metric_descriptors", "endpoint": {"path": "descriptors/metrics", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="confluent_cloud_telemetry_pipeline",
        destination="duckdb",
        dataset_name="confluent_cloud_telemetry_data",
    )
    load_info = pipeline.run(confluent_cloud_telemetry_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_telemetry_pipeline").dataset()
sessions_df = data.query.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM confluent_cloud_telemetry_data.query LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("confluent_cloud_telemetry_pipeline").dataset()
data.query.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 Telemetry 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