Faceberg

Bridge HuggingFace datasets with Apache Iceberg tables — no data copying, just metadata.

Faceberg maps HuggingFace datasets to Apache Iceberg tables. Your catalog metadata lives on HuggingFace Spaces (or Buckets) with an auto-deployed REST API, and any Iceberg-compatible query engine can access the data.

[!NOTE] Faceberg is early-stage, alpha-quality software — APIs and CLI flags may still evolve between releases. See CHANGELOG.md for what's landed so far.

Installation

pip install faceberg

Requires Python 3.10+. The REST catalog server (faceberg serve) is included by default.

Prerequisites

Get a token from HuggingFace Settings and export it:

export HF_TOKEN=your_huggingface_token

The token is only required for remote catalogs (user/repo, hf://...). Local catalogs (plain paths) don't need one.

Quick Start

# Create a catalog on HuggingFace Hub (deploys a Space with a REST API)
faceberg user/mycatalog init

# Add datasets — table identifier is inferred (org.repo) unless --table is given
faceberg user/mycatalog add stanfordnlp/imdb
faceberg user/mycatalog add openai/gsm8k --config main

# List what's in the catalog
faceberg user/mycatalog list

# Query with interactive DuckDB shell
faceberg user/mycatalog quack

SELECT label, substr(text, 1, 100) as preview
FROM faceberg.stanfordnlp.imdb
LIMIT 10;

CLI Reference

Every command follows the same shape: faceberg <catalog-uri> <command> [args], where <catalog-uri> is one of:

Run faceberg <uri> <command> --help for full flag details and examples on any command.

faceberg.yml

Catalogs are described by a faceberg.yml config that tracks dataset-to-table mappings:

default:
  imdb:
    type: dataset
    repo: stanfordnlp/imdb
    config: plain_text
  gsm8k:
    type: dataset
    repo: openai/gsm8k
    config: main

faceberg init tables.yml bootstraps a catalog from a file like this in one shot; faceberg sync re-reads it and updates any table whose source dataset revision changed.

Local Catalogs

For development, testing, or CI, point the CLI at a filesystem path instead of a HuggingFace URI — no token needed:

faceberg ./mycatalog init
faceberg ./mycatalog add stanfordnlp/imdb --config plain_text
faceberg ./mycatalog serve --port 8181   # in one terminal
faceberg ./mycatalog quack               # in another

See Local Catalogs for the on-disk layout and testing patterns.

HuggingFace Buckets

Catalog metadata can also live in a HuggingFace Bucket (hf://buckets/org/name) instead of a Space — an S3-like storage backend with no git history, useful for catalogs that don't need a hosted REST endpoint:

faceberg hf://buckets/user/mycatalog init
faceberg hf://buckets/user/mycatalog add stanfordnlp/imdb

How It Works

HuggingFace Hub
┌─────────────────────────────────────────────────────────┐
│                                                         │
│  ┌─────────────────────┐    ┌─────────────────────────┐ │
│  │  HF Datasets        │    │  HF Spaces (Catalog)    │ │
│  │  (Original Parquet) │◄───│  • Iceberg metadata     │ │
│  │                     │    │  • REST API endpoint    │ │
│  │  stanfordnlp/imdb/  │    │  • faceberg.yml         │ │
│  │   └── *.parquet     │    │                         │ │
│  └─────────────────────┘    └───────────┬─────────────┘ │
│                                         │               │
└─────────────────────────────────────────┼───────────────┘
                                          │ Iceberg REST API
                                          ▼
                              ┌─────────────────────────┐
                              │     Query Engines       │
                              │  DuckDB, Pandas, Spark  │
                              └─────────────────────────┘

No data is copied — only metadata is created. Query with DuckDB, PyIceberg, Spark, or any Iceberg-compatible tool.

Python API

import os
from faceberg import catalog

cat = catalog("user/mycatalog", hf_token=os.environ.get("HF_TOKEN"))
table = cat.load_table("stanfordnlp.imdb")
df = table.scan(limit=100).to_pandas()

The catalog object exposes the usual Iceberg operations:

Pandas integration

import pandas as pd

df = pd.read_iceberg(
    table_identifier="stanfordnlp.imdb",
    catalog_name="faceberg",
    catalog_properties={"type": "rest", "uri": "https://user-mycatalog.hf.space"},
    columns=["text", "label"],
    limit=10,
)

See Pandas Integration for the local-catalog variant and more examples.

Share Your Catalog

Your catalog is accessible to anyone via the REST API:

import duckdb

conn = duckdb.connect()
conn.execute("INSTALL iceberg; LOAD iceberg")
conn.execute("""
    ATTACH 'https://user-mycatalog.hf.space' AS cat (
        TYPE ICEBERG,
        ENDPOINT 'https://user-mycatalog.hf.space',
        AUTHORIZATION_TYPE 'none'
    )
""")

result = conn.execute("SELECT * FROM cat.stanfordnlp.imdb LIMIT 5").fetchdf()

To make your catalog private, set the underlying HuggingFace Space/dataset/Bucket to private.

Documentation

Read the docs →

• Getting Started — Full quickstart guide
• Local Catalogs — Use local catalogs for development
• Buckets — Store catalog metadata in HF Buckets
• DuckDB Integration — Advanced SQL queries
• Pandas Integration — Load into DataFrames
• Architecture — How Faceberg maps datasets to Iceberg metadata

Development

git clone https://github.com/kszucs/faceberg
cd faceberg
pip install -e '.[dev]'

Common tasks are wired up via just (see the justfile):

just test     # run the test suite (pytest faceberg/tests/)
just cov      # run tests with coverage
just format   # format code with ruff
just check    # lint + format check
just build    # build distribution packages

Docs are written in Quarto (docs/*.qmd) and published to https://faceberg.kszucs.dev/.

License

Apache 2.0