Load Hugging Face Datasets data to DuckDB

Hugging Face Datasets Server is a REST API that provides metadata, row previews, search, filtering, and parquet exports for datasets hosted on the Hugging Face Hub. Everything needed to build a working Hugging Face Datasets → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Hugging Face Datasets to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Hugging Face Datasets to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Hugging Face Datasets API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Hugging Face Datasets API at a glance

These values come from the Hugging Face Datasets API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Hugging Face Datasets API?

Authenticated requests require an 'Authorization' header with the value 'Bearer '.

1. Get your credentials

To obtain a Hugging Face API credential (referred to as a User Access Token): 1. Log in to your account at huggingface.co. 2. Click on your profile avatar in the top-right corner and select Settings from the dropdown menu. 3. In the left-hand sidebar, click on Access Tokens. 4. Click the 'New token' button. 5. Enter a descriptive name for your token, select the appropriate permissions (e.g., read, write, or fine-grained), and click 'Generate a token'. 6. Copy the token immediately, as it will be masked after you close the dialog. Store this securely as it is the only time it will be fully displayed.

2. Add them to .dlt/secrets.toml

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[sources.hugging_face_datasets_source]
huggingface_token = "hf_your_token_here"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Hugging Face Datasets data can I load into DuckDB?

These are the Hugging Face Datasets endpoints dlt can load into DuckDB:

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How do I load only new Hugging Face Datasets records?

Hugging Face Datasets exposes lastModified on api/datasets, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "datasets", "endpoint": {
    "path": "api/datasets",
    "incremental": {"cursor_path": "lastModified", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Hugging Face Datasets pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /splits and /first-rows from the Hugging Face Datasets API into DuckDB:

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

@dlt.source
def hugging_face_datasets_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://datasets-server.huggingface.co",
            "auth": {"type": "bearer", "token": api_token},
        },
        "resources": [
            {"name": "datasets", "endpoint": {"path": "api/datasets"}},
            {"name": "rows", "endpoint": {"path": "rows", "data_selector": "rows"}}
        ],
    }
    yield from rest_api_resources(config)


def load_hugging_face_datasets_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="hugging_face_datasets_pipeline",
        destination="duckdb",
        dataset_name="hugging_face_datasets_data",
    )
    load_info = pipeline.run(hugging_face_datasets_source())
    print(load_info)


if __name__ == "__main__":
    load_hugging_face_datasets_to_duckdb()

Run it with python hugging_face_datasets_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Hugging Face Datasets data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("hugging_face_datasets_pipeline").dataset()
df = data.rows.df()
print(df.head())

SQL:

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SELECT * FROM hugging_face_datasets_data.rows LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Hugging Face Datasets to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Hugging Face Datasets loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Hugging Face Datasets data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack