SonarCloud Python API Docs | dltHub

SonarCloud is a cloud‑based code quality and security platform that provides REST APIs to access projects, measures, issues, organizations, users and other SonarQube/SonarCloud resources. The REST API base URL is https://sonarcloud.io/api and all requests require a Bearer token (personal access token) for 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 SonarCloud data in under 10 minutes.

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What data can I load from SonarCloud?

Here are some of the endpoints you can load from SonarCloud:

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How do I authenticate with the SonarCloud API?

SonarCloud Web API uses a personal access token provided as a Bearer token in the Authorization header (Authorization: Bearer ) or as the HTTP basic auth username with an empty password.

1. Get your credentials

1. Sign in to your SonarCloud account at https://sonarcloud.io.
2. Open your account security page (Your avatar → My Account → Security) or go directly to https://sonarcloud.io/account/security/.
3. Click "Generate Tokens", give the token a name, and confirm.
4. Copy the generated token immediately; it will not be shown again.
5. Store the token in your dlt secrets file (secrets.toml) using the key defined for the source (e.g., api_token).

2. Add them to .dlt/secrets.toml

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[sources.sonar_cloud_source]
api_token = "your_sonarcloud_token_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 SonarCloud 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 sonar_cloud_pipeline.py

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

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Pipeline sonar_cloud_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset sonar_cloud_data
The duckdb destination used duckdb:/sonar_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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dlt pipeline sonar_cloud_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 projects and measures from the SonarCloud 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 sonar_cloud_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://sonarcloud.io/api",
            "auth": {
                "type": "bearer",
                "token": api_token,
            },
        },
        "resources": [
            {"name": "projects", "endpoint": {"path": "api/projects/search", "data_selector": "components"}},
            {"name": "measures", "endpoint": {"path": "api/measures/component", "data_selector": "component.measures"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="sonar_cloud_pipeline",
        destination="duckdb",
        dataset_name="sonar_cloud_data",
    )
    load_info = pipeline.run(sonar_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("sonar_cloud_pipeline").dataset()
sessions_df = data.projects.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM sonar_cloud_data.projects LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("sonar_cloud_pipeline").dataset()
data.projects.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 SonarCloud 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

Authentication failures

If you receive 401 Unauthorized, verify that the token is valid and included in the Authorization: Bearer <token> header or as the basic‑auth username with an empty password. Regenerate the token from the SonarCloud account security page if necessary.

Permission errors (403)

A 403 Forbidden response indicates that the token does not have sufficient rights for the requested resource. Ensure the associated user belongs to the appropriate organization/project and has the necessary role.

Rate limiting (429)

SonarCloud may return 429 Too Many Requests when request limits are exceeded. Respect the Retry-After header, implement exponential back‑off, and reduce concurrency for bulk loads.

Pagination

Many search endpoints are paginated using page/pageSize (or p/ps) parameters. The response includes paging metadata such as total, p, and ps. Iterate over pages until all records are retrieved, using the appropriate data selector (e.g., components, issues).

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