Confluent Schema Registry Python API Docs | dltHub

The Confluent Schema Registry REST API allows registration, listing, and deletion of schemas and subjects. It provides endpoints for managing schema versions and compatibility checks. The API uses HTTP methods for CRUD operations on schemas. The REST API base URL is https://<SCHEMA_REGISTRY_ENDPOINT> and Confluent Cloud: Basic auth (API key:secret) or OAuth Bearer token; Platform: optional/no-auth or basic auth if configured..

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 Schema Registry data in under 10 minutes.

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What data can I load from Confluent Schema Registry?

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

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How do I authenticate with the Confluent Schema Registry API?

Confluent Cloud Schema Registry accepts HTTP Basic auth using an SR API key and secret (use -u APIKEY:APISECRET) or OAuth Bearer tokens (Authorization: Bearer ). Self-managed Schema Registry typically runs at a host you control (e.g. https://schemaregistry.example.com) and may be unsecured or configured for basic auth via 'basic.auth.credentials.source' and 'basic.auth.user.info'.

1. Get your credentials

1. In Confluent Cloud Console, pick the Environment > Cluster > Schema Registry > Endpoints.
2. Click 'API keys' (or 'Create API Key for Schema Registry') and create a new API key + secret scoped to Schema Registry.
3. Save API key and secret (secret shown only once). Use them with HTTP Basic auth (username=API_KEY, password=API_SECRET) or configure OAuth token per 'Use OAuth/OIDC to Authenticate to Confluent Cloud' if using OAuth. For self-managed Schema Registry, create or obtain the configured basic auth credentials or use the registry host URL if no auth is configured.

2. Add them to .dlt/secrets.toml

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[sources.confluent_schema_registry_source]
sr_api_key = "<SR_API_KEY>"
sr_api_secret = "<SR_API_SECRET>"

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 Schema Registry 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_schema_registry_pipeline.py

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

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Pipeline confluent_schema_registry_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset confluent_schema_registry_data
The duckdb destination used duckdb:/confluent_schema_registry.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_schema_registry_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 Schema Registry 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_schema_registry_source(sr_api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<SCHEMA_REGISTRY_ENDPOINT>",
            "auth": {
                "type": "http_basic",
                "api_key": sr_api_key,
            },
        },
        "resources": [
            {"name": "subjects", "endpoint": {"path": "subjects"}},
            {"name": "schemas", "endpoint": {"path": "schemas/ids/{id}"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="confluent_schema_registry_pipeline",
        destination="duckdb",
        dataset_name="confluent_schema_registry_data",
    )
    load_info = pipeline.run(confluent_schema_registry_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_schema_registry_pipeline").dataset()
sessions_df = data.subjects.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM confluent_schema_registry_data.subjects LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("confluent_schema_registry_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 Schema Registry 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 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