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 API keys for authentication, sent as an Authorization header..

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 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 can be performed using Basic HTTP authentication with an API Key ID as the username and API Key Secret as the password in the Authorization: Basic {key} header, or by sending a Confluent Security Token Service (STS) access token as a Bearer {confluent-sts-access-token} in the Authorization header.

1. Get your credentials

Confluent Cloud API Keys can be created using the Confluent Cloud CLI. For Prometheus configuration, create a Confluent Cloud API Key/Secret with the MetricsViewer role.

2. Add them to .dlt/secrets.toml

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[sources.confluent_cloud_telemetry_source]
api_key = "your_api_key_id_here"
api_secret = "your_api_key_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 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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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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dlt pipeline confluent_cloud_telemetry_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 metrics_descriptors and resource_descriptors 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",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "metrics_descriptors", "endpoint": {"path": "v2/metrics/{dataset}/descriptors/metrics", "data_selector": "data"}},
            {"name": "resource_descriptors", "endpoint": {"path": "v2/metrics/{dataset}/descriptors/resources", "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.metrics_descriptors.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM confluent_cloud_telemetry_data.metrics_descriptors LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("confluent_cloud_telemetry_pipeline").dataset()
data.metrics_descriptors.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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Troubleshooting

Rate Limiting

There is a global rate limit of 300 requests per IP address per minute. The /v2/metrics/{dataset}/export endpoint has a specific rate limit of 160 requests per resource, per hour, per principal. When a rate limit is breached, an HTTP 429 status code is returned with a body containing an errors array, for example: { "errors" : [ { "status" : "429" , "detail" : "Too many requests..." } ] }.

Pagination

Cursors, tokens, and corresponding pagination links provided in responses may expire after a short period.

USM Dataset Authorization

Requests to the 'USM' dataset require a resource filter (e.g., Kafka cluster ID, Connector ID) in the query for authorization. The client's API key must be authorized for the specific resource referenced in the filter.

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