Getoto Noise Python API Docs | dltHub

Noise is a WordPress site that provides a public REST API for blog content. The REST API base URL is https://noise.getoto.net/wp-json and Public content requires no authentication; optional authentication uses HTTP Basic with a WordPress application password..

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 Getoto Noise data in under 10 minutes.

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What data can I load from Getoto Noise?

Here are some of the endpoints you can load from Getoto Noise:

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How do I authenticate with the Getoto Noise API?

Public GET requests do not require any headers. For protected endpoints, use HTTP Basic with the username and an Application Password (Authorization: Basic base64(username:application_password)).

1. Get your credentials

1. Log in to the WordPress admin dashboard at https://noise.getoto.net/wp-admin.
2. In the left menu, go to Users → Profile.
3. Scroll to the Application Passwords section.
4. Enter a name for the new password and click Add New Application Password.
5. Copy the generated 24‑character password; it will not be shown again.
6. Use this password together with your WordPress username for HTTP Basic authentication.

2. Add them to .dlt/secrets.toml

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[sources.getoto_noise_source]
username = "your_wp_username"
application_password = "your_application_password"

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 Getoto Noise 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 getoto_noise_pipeline.py

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

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Pipeline getoto_noise_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset getoto_noise_data
The duckdb destination used duckdb:/getoto_noise.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 getoto_noise_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 posts and tags from the Getoto Noise 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 getoto_noise_source(application_password=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://noise.getoto.net/wp-json",
            "auth": {
                "type": "http_basic",
                "application_password": application_password,
            },
        },
        "resources": [
            {"name": "posts", "endpoint": {"path": "wp-json/wp/v2/posts"}},
            {"name": "tags", "endpoint": {"path": "wp-json/wp/v2/tags"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="getoto_noise_pipeline",
        destination="duckdb",
        dataset_name="getoto_noise_data",
    )
    load_info = pipeline.run(getoto_noise_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("getoto_noise_pipeline").dataset()
sessions_df = data.posts.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM getoto_noise_data.posts LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("getoto_noise_pipeline").dataset()
data.posts.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 Getoto Noise 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

401 Unauthorized

Occurs when a protected endpoint is called without valid credentials. Fix by adding an Authorization: Basic … header with your username and application password.

403 Forbidden

The authenticated user lacks the required capabilities. Ensure the user role has the necessary permissions for the requested operation.

404 Not Found

The endpoint path or resource ID is incorrect. Verify the URL and ID.

429 Too Many Requests

WordPress or a CDN may enforce rate limits. Implement exponential back‑off retries and respect any Retry‑After header.

Pagination

Collection endpoints support per_page (default 10, max varies) and page query parameters. Pagination metadata is provided via response headers X-WP-Total and X-WP-TotalPages. Loop until all pages are retrieved.

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