Bunny.net Python API Docs | dltHub

Bunny.net is a CDN and cloud storage platform providing APIs to manage pull zones, storage zones, streaming/video libraries, DNS, statistics, billing and related resources. The REST API base URL is https://api.bunny.net and All core API requests require an AccessKey header for authentication; storage HTTP requests use the storage zone password in the AccessKey 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 Bunny.net data in under 10 minutes.

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What data can I load from Bunny.net?

Here are some of the endpoints you can load from Bunny.net:

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How do I authenticate with the Bunny.net API?

Set the HTTP header 'AccessKey' to your account API key for Core/Stream/API actions. For Edge Storage operations, set 'AccessKey' to your storage zone password and use the storage endpoint host matching your zone's region.

1. Get your credentials

1. Log in to https://dash.bunny.net. 2. Go to Account > API Keys (or Account Settings). 3. Copy the Account API Key for Core API actions. 4. For storage HTTP access, go to Storage > select your Storage Zone and copy/set the Storage Zone Password. 5. Keep keys secret; use AccessKey header in requests.

2. Add them to .dlt/secrets.toml

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[sources.bunnynet_source]
api_key = "YOUR_ACCOUNT_API_KEY"
storage_password = "YOUR_STORAGE_ZONE_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 Bunny.net 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 bunnynet_pipeline.py

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

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Pipeline bunnynet_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset bunnynet_data
The duckdb destination used duckdb:/bunnynet.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 bunnynet_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 pull_zones and storage_files from the Bunny.net 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 bunnynet_source(access_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.bunny.net",
            "auth": {
                "type": "api_key",
                "api_key": access_key,
            },
        },
        "resources": [
            {"name": "pull_zones", "endpoint": {"path": "pullzone", "data_selector": "Items"}},
            {"name": "storage_files", "endpoint": {"path": "storagezone/{storageZoneId}/files", "data_selector": "Items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="bunnynet_pipeline",
        destination="duckdb",
        dataset_name="bunnynet_data",
    )
    load_info = pipeline.run(bunnynet_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("bunnynet_pipeline").dataset()
sessions_df = data.pull_zones.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM bunnynet_data.pull_zones LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("bunnynet_pipeline").dataset()
data.pull_zones.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 Bunny.net 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 the 'AccessKey' header value. Core API requires the account API key; Edge Storage HTTP uses the storage zone password as the AccessKey. Ensure no extra whitespace and that the key is active in the dashboard.

Rate limiting (429)

The API returns 429 when rate limits are exceeded. Implement exponential backoff and respect per-account throttling. Check response headers for rate limit details when present.

Pagination quirks

List endpoints return an object with 'Items', 'CurrentPage', 'TotalItems' and 'HasMoreItems'. Use page and perPage query parameters to iterate; stop when HasMoreItems is false or when CurrentPage*perPage >= TotalItems.

Error body format

Errors return JSON like { "ErrorKey": "pullZone.not_found", "Field": "PullZone", "Message": "The requested Pull Zone was not found" } with HTTP status codes (400,401,403,404,429,500).

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