Apache kafka Python API Docs | dltHub

Confluent REST Proxy is an HTTP interface for producing and consuming messages to an Apache Kafka® cluster. The REST API base URL is https://localhost:8082 and Authentication is optional; when enabled, the API uses either Basic 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 Apache kafka data in under 10 minutes.

---

What data can I load from Apache kafka?

Here are some of the endpoints you can load from Apache kafka:

---

How do I authenticate with the Apache kafka API?

Include an Authorization header: either "Basic <base64‑credentials>" or "Bearer <access_token>" depending on the server configuration.

1. Get your credentials

1. Log in to the Confluent Cloud console.
2. Navigate to the "API keys" section under your cluster settings.
3. Click "Create API key" for the desired service account.
4. Copy the generated "API key" and "API secret"; the key will be used as the username and the secret as the password for Basic auth or to obtain a Bearer token.
5. Store the credentials securely for use in dlt configuration.

2. Add them to .dlt/secrets.toml

Copy
[sources.apache_kafka_source]
api_key = "your_api_key_here"
password = "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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 Apache kafka 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:

Copy
python apache_kafka_pipeline.py

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

Copy
Pipeline apache_kafka_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset apache_kafka_data
The duckdb destination used duckdb:/apache_kafka.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
dlt pipeline apache_kafka_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.

---

Python pipeline example

This example loads topics and consumers from the Apache kafka API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def apache_kafka_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://localhost:8082",
            "auth": {
                "type": "basic",
                "password": api_key,
            },
        },
        "resources": [
            {"name": "topics", "endpoint": {"path": "topics"}},
            {"name": "consumers", "endpoint": {"path": "consumers/{group}/instances/{instance}/records", "data_selector": "records"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="apache_kafka_pipeline",
        destination="duckdb",
        dataset_name="apache_kafka_data",
    )
    load_info = pipeline.run(apache_kafka_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.

---

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):

Copy
import dlt

data = dlt.pipeline("apache_kafka_pipeline").dataset()
sessions_df = data.consumers.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM apache_kafka_data.consumers LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("apache_kafka_pipeline").dataset()
data.consumers.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

---

What destinations can I load Apache kafka 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.

---

Troubleshooting

Authentication errors

If the Authorization header is missing or invalid, the API returns 401 Unauthorized. Ensure your API key and secret are correct and that the header is properly formatted.

Conflict errors

Creating a consumer instance with a name that already exists returns 409 Conflict. Use a unique consumer instance name or delete the existing one before retrying.

Rate limiting

When the server limits request volume, it responds with 429 Too Many Requests. Implement exponential back‑off and respect the Retry-After header.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

---

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.

Copy
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