diff --git a/pyproject.toml b/pyproject.toml
index 79b722e..aba295e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -15,6 +15,8 @@ datasets = "^3.2"
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^8.0.0"
+pytest-dotenv = "^0.5.2"
+pytest-mock = "^3.14.0"
 
 [build-system]
 requires = ["poetry-core"]
diff --git a/pyspark_huggingface/huggingface_sink.py b/pyspark_huggingface/huggingface_sink.py
new file mode 100644
index 0000000..7e6689c
--- /dev/null
+++ b/pyspark_huggingface/huggingface_sink.py
@@ -0,0 +1,301 @@
+import ast
+import logging
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Iterator, List, Optional
+
+from pyspark.sql.datasource import (
+    DataSource,
+    DataSourceArrowWriter,
+    WriterCommitMessage,
+)
+from pyspark.sql.types import StructType
+
+if TYPE_CHECKING:
+    from huggingface_hub import CommitOperationAdd, CommitOperationDelete
+    from pyarrow import RecordBatch
+
+logger = logging.getLogger(__name__)
+
+class HuggingFaceSink(DataSource):
+    """
+    A DataSource for writing Spark DataFrames to HuggingFace Datasets.
+
+    This data source allows writing Spark DataFrames to the HuggingFace Hub as Parquet files.
+
+    Name: `huggingfacesink`
+
+    Data Source Options:
+    - token (str, required): HuggingFace API token for authentication.
+    - path (str, required): HuggingFace repository ID, e.g. `{username}/{dataset}`.
+    - path_in_repo (str): Path within the repository to write the data. Defaults to the root.
+    - split (str): Split name to write the data to. Defaults to `train`. Only `train`, `test`, and `validation` are supported.
+    - revision (str): Branch, tag, or commit to write to. Defaults to the main branch.
+    - endpoint (str): Custom HuggingFace API endpoint URL.
+    - max_bytes_per_file (int): Maximum size of each Parquet file.
+    - row_group_size (int): Row group size when writing Parquet files.
+    - max_operations_per_commit (int): Maximum number of files to add/delete per commit.
+
+    Modes:
+    - `overwrite`: Overwrite an existing dataset by deleting existing Parquet files.
+    - `append`: Append data to an existing dataset.
+
+    Examples
+    --------
+
+    Write a DataFrame to the HuggingFace Hub.
+
+    >>> df.write.format("huggingfacesink").mode("overwrite").options(token="...").save("user/dataset")
+
+    Append to an existing dataset on the HuggingFace Hub.
+
+    >>> df.write.format("huggingfacesink").mode("append").options(token="...").save("user/dataset")
+
+    Write to the `test` split of a dataset.
+
+    >>> df.write.format("huggingfacesink").mode("overwrite").options(token="...", split="test").save("user/dataset")
+    """
+
+    def __init__(self, options):
+        super().__init__(options)
+
+        if "path" not in options or not options["path"]:
+            raise Exception("You must specify a dataset name.")
+
+        kwargs = dict(self.options)
+        self.token = kwargs.pop("token")
+        self.repo_id = kwargs.pop("path")
+        self.path_in_repo = kwargs.pop("path_in_repo", None)
+        self.split = kwargs.pop("split", None)
+        self.revision = kwargs.pop("revision", None)
+        self.endpoint = kwargs.pop("endpoint", None)
+        for arg in kwargs:
+            if kwargs[arg].lower() == "true":
+                kwargs[arg] = True
+            elif kwargs[arg].lower() == "false":
+                kwargs[arg] = False
+            else:
+                try:
+                    kwargs[arg] = ast.literal_eval(kwargs[arg])
+                except ValueError:
+                    pass
+        self.kwargs = kwargs
+
+    @classmethod
+    def name(cls):
+        return "huggingfacesink"
+
+    def writer(self, schema: StructType, overwrite: bool) -> DataSourceArrowWriter:
+        return HuggingFaceDatasetsWriter(
+            repo_id=self.repo_id,
+            path_in_repo=self.path_in_repo,
+            split=self.split,
+            revision=self.revision,
+            schema=schema,
+            overwrite=overwrite,
+            token=self.token,
+            endpoint=self.endpoint,
+            **self.kwargs,
+        )
+
+
+@dataclass
+class HuggingFaceCommitMessage(WriterCommitMessage):
+    additions: List["CommitOperationAdd"]
+
+
+class HuggingFaceDatasetsWriter(DataSourceArrowWriter):
+    repo_type = "dataset"
+
+    def __init__(
+        self,
+        *,
+        repo_id: str,
+        path_in_repo: Optional[str] = None,
+        split: Optional[str] = None,
+        revision: Optional[str] = None,
+        schema: StructType,
+        overwrite: bool,
+        token: str,
+        endpoint: Optional[str] = None,
+        row_group_size: Optional[int] = None,
+        max_bytes_per_file=500_000_000,
+        max_operations_per_commit=100,
+        **kwargs,
+    ):
+        import uuid
+
+        self.repo_id = repo_id
+        self.path_in_repo = (path_in_repo or "").strip("/")
+        self.split = split or "train"
+        self.revision = revision
+        self.schema = schema
+        self.overwrite = overwrite
+        self.token = token
+        self.endpoint = endpoint
+        self.row_group_size = row_group_size
+        self.max_bytes_per_file = max_bytes_per_file
+        self.max_operations_per_commit = max_operations_per_commit
+        self.kwargs = kwargs
+
+        # Use a unique filename prefix to avoid conflicts with existing files
+        self.uuid = uuid.uuid4()
+
+        self.validate()
+
+    def validate(self):
+        if self.split not in ["train", "test", "validation"]:
+            """
+            TODO: Add support for custom splits.
+
+            For custom split names to be recognized, the files must have path with format:
+            `data/{split}-{iiiii}-of-{nnnnn}.parquet`
+            where `iiiii` is the part number and `nnnnn` is the total number of parts, both padded to 5 digits.
+            Example: `data/custom-00000-of-00002.parquet`
+
+            Therefore the current usage of UUID to avoid naming conflicts won't work for custom split names.
+            To fix this we can rename the files in the commit phase to satisfy the naming convention.
+            """
+            raise NotImplementedError(
+                f"Only 'train', 'test', and 'validation' splits are supported. Got '{self.split}'."
+            )
+
+    def get_api(self):
+        from huggingface_hub import HfApi
+
+        return HfApi(token=self.token, endpoint=self.endpoint)
+
+    @property
+    def prefix(self) -> str:
+        return f"{self.path_in_repo}/{self.split}".strip("/")
+
+    def get_delete_operations(self) -> Iterator["CommitOperationDelete"]:
+        """
+        Get the commit operations to delete all existing Parquet files.
+        This is used when `overwrite=True` to clear the target directory.
+        """
+        from huggingface_hub import CommitOperationDelete
+        from huggingface_hub.errors import EntryNotFoundError
+        from huggingface_hub.hf_api import RepoFolder
+
+        api = self.get_api()
+
+        try:
+            objects = api.list_repo_tree(
+                path_in_repo=self.path_in_repo,
+                repo_id=self.repo_id,
+                repo_type=self.repo_type,
+                revision=self.revision,
+                expand=False,
+                recursive=False,
+            )
+            for obj in objects:
+                if obj.path.startswith(self.prefix):
+                    yield CommitOperationDelete(
+                        path_in_repo=obj.path, is_folder=isinstance(obj, RepoFolder)
+                    )
+        except EntryNotFoundError as e:
+            logger.info(f"Writing to a new path: {e}")
+
+    def write(self, iterator: Iterator["RecordBatch"]) -> HuggingFaceCommitMessage:
+        import io
+
+        from huggingface_hub import CommitOperationAdd
+        from pyarrow import parquet as pq
+        from pyspark import TaskContext
+        from pyspark.sql.pandas.types import to_arrow_schema
+
+        # Get the current partition ID. Use this to generate unique filenames for each partition.
+        context = TaskContext.get()
+        partition_id = context.partitionId() if context else 0
+
+        api = self.get_api()
+
+        schema = to_arrow_schema(self.schema)
+        num_files = 0
+        additions = []
+
+        # TODO: Evaluate the performance of using a temp file instead of an in-memory buffer.
+        with io.BytesIO() as parquet:
+
+            def flush(writer: pq.ParquetWriter):
+                """
+                Upload the current Parquet file and reset the buffer.
+                """
+                writer.close()  # Close the writer to flush the buffer
+                nonlocal num_files
+                name = (
+                    f"{self.prefix}-{self.uuid}-part-{partition_id}-{num_files}.parquet"
+                )
+                num_files += 1
+                parquet.seek(0)
+
+                addition = CommitOperationAdd(
+                    path_in_repo=name, path_or_fileobj=parquet
+                )
+                api.preupload_lfs_files(
+                    repo_id=self.repo_id,
+                    additions=[addition],
+                    repo_type=self.repo_type,
+                    revision=self.revision,
+                )
+                additions.append(addition)
+
+                # Reuse the buffer for the next file
+                parquet.seek(0)
+                parquet.truncate()
+
+            """
+            Write the Parquet files, flushing the buffer when the file size exceeds the limit.
+            Limiting the size is necessary because we are writing them in memory.
+            """
+            while True:
+                with pq.ParquetWriter(parquet, schema, **self.kwargs) as writer:
+                    num_batches = 0
+                    for batch in iterator:  # Start iterating from where we left off
+                        writer.write_batch(batch, row_group_size=self.row_group_size)
+                        num_batches += 1
+                        if parquet.tell() > self.max_bytes_per_file:
+                            flush(writer)
+                            break  # Start a new file
+                    else:  # Finished writing all batches
+                        if num_batches > 0:
+                            flush(writer)
+                        break  # Exit while loop
+
+        return HuggingFaceCommitMessage(additions=additions)
+
+    def commit(self, messages: List[HuggingFaceCommitMessage]) -> None:  # type: ignore[override]
+        import math
+
+        api = self.get_api()
+        operations = [
+            addition for message in messages for addition in message.additions
+        ]
+        if self.overwrite:  # Delete existing files if overwrite is enabled
+            operations.extend(self.get_delete_operations())
+
+        """
+        Split the commit into multiple parts if necessary.
+        The HuggingFace API may time out if there are too many operations in a single commit.
+        """
+        num_commits = math.ceil(len(operations) / self.max_operations_per_commit)
+        for i in range(num_commits):
+            begin = i * self.max_operations_per_commit
+            end = (i + 1) * self.max_operations_per_commit
+            part = operations[begin:end]
+            commit_message = "Upload using PySpark" + (
+                f" (part {i:05d}-of-{num_commits:05d})" if num_commits > 1 else ""
+            )
+            api.create_commit(
+                repo_id=self.repo_id,
+                repo_type=self.repo_type,
+                revision=self.revision,
+                operations=part,
+                commit_message=commit_message,
+            )
+
+    def abort(self, messages: List[HuggingFaceCommitMessage]) -> None:  # type: ignore[override]
+        # We don't need to do anything here, as the files are not included in the repo until commit
+        additions = [addition for message in messages for addition in message.additions]
+        for addition in additions:
+            logger.info(f"Aborted {addition}")
diff --git a/tests/test_huggingface_writer.py b/tests/test_huggingface_writer.py
new file mode 100644
index 0000000..734621b
--- /dev/null
+++ b/tests/test_huggingface_writer.py
@@ -0,0 +1,126 @@
+import os
+import uuid
+
+import pytest
+from pyspark.sql import DataFrame, SparkSession
+from pyspark.testing import assertDataFrameEqual
+from pytest_mock import MockerFixture
+
+
+# ============== Fixtures & Helpers ==============
+
+@pytest.fixture(scope="session")
+def spark():
+    from pyspark_huggingface.huggingface_sink import HuggingFaceSink
+
+    spark = SparkSession.builder.getOrCreate()
+    spark.dataSource.register(HuggingFaceSink)
+    yield spark
+
+
+def token():
+    return os.environ["HF_TOKEN"]
+
+
+def reader(spark):
+    return spark.read.format("huggingface").option("token", token())
+
+
+def writer(df: DataFrame):
+    return df.write.format("huggingfacesink").option("token", token())
+
+
+@pytest.fixture(scope="session")
+def random_df(spark: SparkSession):
+    from pyspark.sql.functions import rand
+
+    return lambda n: spark.range(n).select((rand()).alias("value"))
+
+
+@pytest.fixture(scope="session")
+def api():
+    import huggingface_hub
+
+    return huggingface_hub.HfApi(token=token())
+
+
+@pytest.fixture(scope="session")
+def username(api):
+    return api.whoami()["name"]
+
+
+@pytest.fixture
+def repo(api, username):
+    repo_id = f"{username}/test-{uuid.uuid4()}"
+    api.create_repo(repo_id, private=False, repo_type="dataset")
+    yield repo_id
+    api.delete_repo(repo_id, repo_type="dataset")
+
+
+# ============== Tests ==============
+
+def test_basic(spark, repo, random_df):
+    df = random_df(10)
+    writer(df).mode("append").save(repo)
+    actual = reader(spark).load(repo)
+    assertDataFrameEqual(df, actual)
+
+
+def test_append(spark, repo, random_df):
+    df1 = random_df(10)
+    df2 = random_df(10)
+    writer(df1).mode("append").save(repo)
+    writer(df2).mode("append").save(repo)
+    actual = reader(spark).load(repo)
+    expected = df1.union(df2)
+    assertDataFrameEqual(actual, expected)
+
+
+def test_overwrite(spark, repo, random_df):
+    df1 = random_df(10)
+    df2 = random_df(10)
+    writer(df1).mode("append").save(repo)
+    writer(df2).mode("overwrite").save(repo)
+    actual = reader(spark).load(repo)
+    assertDataFrameEqual(actual, df2)
+
+
+def test_split(spark, repo, random_df):
+    df1 = random_df(10)
+    df2 = random_df(10)
+    writer(df1).mode("append").save(repo)
+    writer(df2).mode("append").options(split="test").save(repo)
+    actual1 = reader(spark).options(split="train").load(repo)
+    actual2 = reader(spark).options(split="test").load(repo)
+    assertDataFrameEqual(actual1, df1)
+    assertDataFrameEqual(actual2, df2)
+
+
+def test_revision(repo, random_df, api):
+    df = random_df(10)
+    api.create_branch(repo, branch="test", repo_type="dataset")
+    writer(df).mode("append").options(revision="test").save(repo)
+    assert any(
+        file.path.endswith(".parquet")
+        for file in api.list_repo_tree(
+            repo, repo_type="dataset", revision="test", recursive=True
+        )
+    )
+
+
+def test_max_bytes_per_file(spark, mocker: MockerFixture):
+    from pyspark_huggingface.huggingface_sink import HuggingFaceDatasetsWriter
+
+    repo = "user/test"
+    api = mocker.patch("huggingface_hub.HfApi").return_value = mocker.MagicMock()
+
+    df = spark.range(10)
+    writer = HuggingFaceDatasetsWriter(
+        repo_id=repo,
+        overwrite=False,
+        schema=df.schema,
+        token="token",
+        max_bytes_per_file=1,
+    )
+    writer.write(iter(df.toArrow().to_batches(max_chunksize=1)))
+    assert api.preupload_lfs_files.call_count == 10