diff --git a/src/datasets/packaged_modules/parquet/parquet.py b/src/datasets/packaged_modules/parquet/parquet.py
index 10797753657..9921a2be6b9 100644
--- a/src/datasets/packaged_modules/parquet/parquet.py
+++ b/src/datasets/packaged_modules/parquet/parquet.py
@@ -15,12 +15,63 @@
 
 @dataclass
 class ParquetConfig(datasets.BuilderConfig):
-    """BuilderConfig for Parquet."""
+    """
+    BuilderConfig for Parquet.
+
+    Args:
+        batch_size (`int`, *optional*):
+            Size of the RecordBatches to iterate on.
+            The default is the row group size (defined by the first row group).
+        columns (`list[str]`, *optional*)
+            List of columns to load, the other ones are ignored.
+            All columns are loaded by default.
+        features: (`Features`, *optional*):
+            Cast the data to `features`.
+        filters (`Union[pyarrow.dataset.Expression, list[tuple], list[list[tuple]]]`, *optional*):
+            Return only the rows matching the filter.
+            If possible the predicate will be pushed down to exploit the partition information
+            or internal metadata found in the data source, e.g. Parquet statistics.
+            Otherwise filters the loaded RecordBatches before yielding them.
+        fragment_scan_options (`pyarrow.dataset.ParquetFragmentScanOptions`)
+            Scan-specific options for Parquet fragments.
+            This is especially useful to configure buffering and caching.
+
+    Example:
+
+    Load a subset of columns:
+
+    ```python
+    >>> ds = load_dataset(parquet_dataset_id, columns=["col_0", "col_1"])
+    ```
+
+    Stream data and efficiently filter data, possibly skipping entire files or row groups:
+
+    ```python
+    >>> filters = [("col_0", "==", 0)]
+    >>> ds = load_dataset(parquet_dataset_id, streaming=True, filters=filters)
+    ```
+
+    Increase the minimum request size when streaming from 32MiB (default) to 128MiB and enable prefetching:
+
+    ```python
+    >>> import pyarrow
+    >>> import pyarrow.dataset
+    >>> fragment_scan_options = pyarrow.dataset.ParquetFragmentScanOptions(
+    ...     cache_options=pyarrow.CacheOptions(
+    ...         prefetch_limit=1,
+    ...         range_size_limit=128 << 20
+    ...     ),
+    ... )
+    >>> ds = load_dataset(parquet_dataset_id, streaming=True, fragment_scan_options=fragment_scan_options)
+    ```
+
+    """
 
     batch_size: Optional[int] = None
     columns: Optional[list[str]] = None
     features: Optional[datasets.Features] = None
     filters: Optional[Union[ds.Expression, list[tuple], list[list[tuple]]]] = None
+    fragment_scan_options: Optional[ds.ParquetFragmentScanOptions] = None
 
     def __post_init__(self):
         super().__post_init__()
@@ -84,9 +135,10 @@ def _generate_tables(self, files):
             if isinstance(self.config.filters, list)
             else self.config.filters
         )
+        parquet_file_format = ds.ParquetFileFormat(default_fragment_scan_options=self.config.fragment_scan_options)
         for file_idx, file in enumerate(itertools.chain.from_iterable(files)):
             with open(file, "rb") as f:
-                parquet_fragment = ds.ParquetFileFormat().make_fragment(f)
+                parquet_fragment = parquet_file_format.make_fragment(f)
                 if parquet_fragment.row_groups:
                     batch_size = self.config.batch_size or parquet_fragment.row_groups[0].num_rows
                     try: