@@ -365,6 +365,17 @@ for buf in tbl.scan().to_arrow_batch_reader():
     print(f"Buffer contains {len(buf)} rows")
 ```
 
+### Streaming writes from a `RecordBatchReader`
+
+`tbl.append()` and `tbl.overwrite()` also accept a `pyarrow.RecordBatchReader` directly, which lets you write datasets that don't fit in memory without materialising them as a `pa.Table` first. PyIceberg consumes the reader once and microbatches it into Parquet files of approximately `write.target-file-size-bytes` (default 512 MiB), keeping memory usage bounded by the target size. All files are committed in a single snapshot.
+
+```python
+reader = pa.RecordBatchReader.from_batches(schema, batch_iter)
+tbl.append(reader)
+```
+
+Streaming writes are currently only supported on **unpartitioned** tables. For a partitioned table, materialise the reader as a `pa.Table` first, or follow [#2152](https://github.com/apache/iceberg-python/issues/2152) for the partitioned support tracked as a follow-up.
+
 To avoid any type inconsistencies during writing, you can convert the Iceberg table schema to Arrow:
 
 ```python
 
@@ -2681,6 +2681,32 @@ def bin_pack_arrow_table(tbl: pa.Table, target_file_size: int) -> Iterator[list[
     return bin_packed_record_batches
 
 
+def bin_pack_record_batches(batches: Iterable[pa.RecordBatch], target_file_size: int) -> Iterator[list[pa.RecordBatch]]:
+    """Microbatch a single-pass stream of RecordBatches into target-sized groups.
+
+    Unlike :func:`bin_pack_arrow_table`, this consumes ``batches`` lazily and
+    holds at most one in-flight buffer in memory, bounded by ``target_file_size``.
+    Suitable for streaming inputs (``pa.RecordBatchReader``,
+    ``Iterator[pa.RecordBatch]``) where the total size is unknown up front and
+    the caller cannot afford to materialise the full dataset.
+
+    Each yielded list of batches is intended to be written as a single Parquet
+    data file. Because this is single-pass FIFO accumulation (no lookback), the
+    last bin may be smaller than ``target_file_size``.
+    """
+    buffer: list[pa.RecordBatch] = []
+    buffer_bytes = 0
+    for batch in batches:
+        buffer.append(batch)
+        buffer_bytes += batch.nbytes
+        if buffer_bytes >= target_file_size:
+            yield buffer
+            buffer = []
+            buffer_bytes = 0
+    if buffer:
+        yield buffer
+
+
 def _check_pyarrow_schema_compatible(
     requested_schema: Schema,
     provided_schema: pa.Schema,
@@ -2800,15 +2826,24 @@ def _get_parquet_writer_kwargs(table_properties: Properties) -> dict[str, Any]:
 
 def _dataframe_to_data_files(
     table_metadata: TableMetadata,
-    df: pa.Table,
+    df: pa.Table | pa.RecordBatchReader,
     io: FileIO,
     write_uuid: uuid.UUID | None = None,
     counter: itertools.count[int] | None = None,
 ) -> Iterable[DataFile]:
-    """Convert a PyArrow table into a DataFile.
+    """Convert a PyArrow Table or RecordBatchReader into DataFiles.
+
+    For a ``pa.Table`` the data is materialised in memory and bin-packed into
+    target-sized files (with partition splitting if the table is partitioned).
+
+    For a ``pa.RecordBatchReader`` batches are streamed and microbatched into
+    target-sized files using bounded memory (see :func:`bin_pack_record_batches`).
+    Streaming writes are currently only supported on unpartitioned tables;
+    partitioned support is tracked in
+    https://github.com/apache/iceberg-python/issues/2152.
 
     Returns:
-        An iterable that supplies datafiles that represent the table.
+        An iterable that supplies datafiles that represent the input data.
     """
     from pyiceberg.table import DOWNCAST_NS_TIMESTAMP_TO_US_ON_WRITE, TableProperties, WriteTask
 
@@ -2828,6 +2863,23 @@ def _dataframe_to_data_files(
         format_version=table_metadata.format_version,
     )
 
+    if isinstance(df, pa.RecordBatchReader):
+        if not table_metadata.spec().is_unpartitioned():
+            raise NotImplementedError(
+                "Writing a pa.RecordBatchReader to a partitioned table is not yet supported. "
+                "Materialise the reader as a pa.Table first, or follow "
+                "https://github.com/apache/iceberg-python/issues/2152 for partitioned streaming support."
+            )
+        yield from write_file(
+            io=io,
+            table_metadata=table_metadata,
+            tasks=(
+                WriteTask(write_uuid=write_uuid, task_id=next(counter), record_batches=batches, schema=task_schema)
+                for batches in bin_pack_record_batches(df, target_file_size)
+            ),
+        )
+        return
+
     if table_metadata.spec().is_unpartitioned():
         yield from write_file(
             io=io,
 
@@ -450,12 +450,45 @@ def update_statistics(self) -> UpdateStatistics:
         """
         return UpdateStatistics(transaction=self)
 
-    def append(self, df: pa.Table, snapshot_properties: dict[str, str] = EMPTY_DICT, branch: str | None = MAIN_BRANCH) -> None:
+    def append(
+        self,
+        df: pa.Table | pa.RecordBatchReader,
+        snapshot_properties: dict[str, str] = EMPTY_DICT,
+        branch: str | None = MAIN_BRANCH,
+    ) -> None:
         """
-        Shorthand API for appending a PyArrow table to a table transaction.
+        Shorthand API for appending PyArrow data to a table transaction.
+
+        Accepts either a fully materialised ``pa.Table`` or a streaming
+        ``pa.RecordBatchReader``. Streaming is microbatched by
+        ``write.target-file-size-bytes`` so memory stays bounded; the reader is
+        consumed once and cannot be reused.
+
+        Streaming writes are currently only supported on unpartitioned tables;
+        passing a ``pa.RecordBatchReader`` for a partitioned table raises
+        ``NotImplementedError``. See
+        https://github.com/apache/iceberg-python/issues/2152.
+
+        Note:
+            When ``df`` is a ``pa.RecordBatchReader`` the reader is consumed
+            once and cannot be replayed. If the catalog commit fails (e.g.
+            ``CommitFailedException`` from a concurrent writer) the reader is
+            already drained and a naive retry will append zero rows. Callers
+            that need at-least-once semantics should either:
+
+            - reconstruct the reader on each attempt via a factory callable,
+              or
+            - use a two-stage pattern — write Parquet files explicitly and
+              then call :meth:`add_files` (whose input is a replayable list of
+              paths) within a retry loop.
+
+            Failures during the write stage (mid-stream reader exception, S3
+            errors) do not commit a snapshot, but may leave orphan data files
+            in storage that are not referenced by any snapshot. Clean these
+            up with expire/orphan-file maintenance jobs.
 
         Args:
-            df: The Arrow dataframe that will be appended to overwrite the table
+            df: An Arrow Table or a RecordBatchReader of records to append.
             snapshot_properties: Custom properties to be added to the snapshot summary
             branch: Branch Reference to run the append operation
         """
@@ -466,8 +499,8 @@ def append(self, df: pa.Table, snapshot_properties: dict[str, str] = EMPTY_DICT,
 
         from pyiceberg.io.pyarrow import _check_pyarrow_schema_compatible, _dataframe_to_data_files
 
-        if not isinstance(df, pa.Table):
-            raise ValueError(f"Expected PyArrow table, got: {df}")
+        if not isinstance(df, (pa.Table, pa.RecordBatchReader)):
+            raise ValueError(f"Expected pa.Table or pa.RecordBatchReader, got: {df}")
 
         downcast_ns_timestamp_to_us = Config().get_bool(DOWNCAST_NS_TIMESTAMP_TO_US_ON_WRITE) or False
         _check_pyarrow_schema_compatible(
@@ -478,12 +511,14 @@ def append(self, df: pa.Table, snapshot_properties: dict[str, str] = EMPTY_DICT,
         )
 
         with self._append_snapshot_producer(snapshot_properties, branch=branch) as append_files:
-            # skip writing data files if the dataframe is empty
-            if df.shape[0] > 0:
-                data_files = list(
-                    _dataframe_to_data_files(
-                        table_metadata=self.table_metadata, write_uuid=append_files.commit_uuid, df=df, io=self._table.io
-                    )
+            # For pa.Table we can short-circuit empty inputs cheaply. For a
+            # RecordBatchReader the stream is consumed lazily by
+            # _dataframe_to_data_files and an empty reader simply yields zero
+            # data files (the snapshot is still committed for symmetry with the
+            # pa.Table case where empty inputs also produce a snapshot).
+            if isinstance(df, pa.RecordBatchReader) or df.shape[0] > 0:
+                data_files = _dataframe_to_data_files(
+                    table_metadata=self.table_metadata, write_uuid=append_files.commit_uuid, df=df, io=self._table.io
                 )
                 for data_file in data_files:
                     append_files.append_data_file(data_file)
@@ -555,14 +590,42 @@ def dynamic_partition_overwrite(
 
     def overwrite(
         self,
-        df: pa.Table,
+        df: pa.Table | pa.RecordBatchReader,
         overwrite_filter: BooleanExpression | str = ALWAYS_TRUE,
         snapshot_properties: dict[str, str] = EMPTY_DICT,
         case_sensitive: bool = True,
         branch: str | None = MAIN_BRANCH,
     ) -> None:
         """
-        Shorthand for adding a table overwrite with a PyArrow table to the transaction.
+        Shorthand for adding a table overwrite with a PyArrow table or RecordBatchReader to the transaction.
+
+        Accepts either a fully materialised ``pa.Table`` or a streaming
+        ``pa.RecordBatchReader``. Streaming is microbatched by
+        ``write.target-file-size-bytes`` so memory stays bounded; the reader is
+        consumed once and cannot be reused.
+
+        Streaming writes are currently only supported on unpartitioned tables;
+        passing a ``pa.RecordBatchReader`` for a partitioned table raises
+        ``NotImplementedError``. See
+        https://github.com/apache/iceberg-python/issues/2152.
+
+        Note:
+            When ``df`` is a ``pa.RecordBatchReader`` the reader is consumed
+            once and cannot be replayed. If the catalog commit fails (e.g.
+            ``CommitFailedException`` from a concurrent writer) the reader is
+            already drained and a naive retry will write zero rows. Callers
+            that need at-least-once semantics should either:
+
+            - reconstruct the reader on each attempt via a factory callable,
+              or
+            - use a two-stage pattern — write Parquet files explicitly and
+              then call :meth:`add_files` (whose input is a replayable list
+              of paths) within a retry loop.
+
+            Failures during the write stage (mid-stream reader exception, S3
+            errors) do not commit a snapshot, but may leave orphan data files
+            in storage that are not referenced by any snapshot. Clean these
+            up with expire/orphan-file maintenance jobs.
 
         An overwrite may produce zero or more snapshots based on the operation:
 
@@ -571,7 +634,7 @@ def overwrite(
             - APPEND: In case new data is being inserted into the table.
 
         Args:
-            df: The Arrow dataframe that will be used to overwrite the table
+            df: An Arrow Table or a RecordBatchReader of records to write.
             overwrite_filter: ALWAYS_TRUE when you overwrite all the data,
                               or a boolean expression in case of a partial overwrite
             snapshot_properties: Custom properties to be added to the snapshot summary
@@ -585,8 +648,8 @@ def overwrite(
 
         from pyiceberg.io.pyarrow import _check_pyarrow_schema_compatible, _dataframe_to_data_files
 
-        if not isinstance(df, pa.Table):
-            raise ValueError(f"Expected PyArrow table, got: {df}")
+        if not isinstance(df, (pa.Table, pa.RecordBatchReader)):
+            raise ValueError(f"Expected pa.Table or pa.RecordBatchReader, got: {df}")
 
         downcast_ns_timestamp_to_us = Config().get_bool(DOWNCAST_NS_TIMESTAMP_TO_US_ON_WRITE) or False
         _check_pyarrow_schema_compatible(
@@ -606,8 +669,8 @@ def overwrite(
             )
 
         with self._append_snapshot_producer(snapshot_properties, branch=branch) as append_files:
-            # skip writing data files if the dataframe is empty
-            if df.shape[0] > 0:
+            # See append() for the empty-input handling rationale.
+            if isinstance(df, pa.RecordBatchReader) or df.shape[0] > 0:
                 data_files = _dataframe_to_data_files(
                     table_metadata=self.table_metadata, write_uuid=append_files.commit_uuid, df=df, io=self._table.io
                 )
@@ -1373,12 +1436,21 @@ def upsert(
                 snapshot_properties=snapshot_properties,
             )
 
-    def append(self, df: pa.Table, snapshot_properties: dict[str, str] = EMPTY_DICT, branch: str | None = MAIN_BRANCH) -> None:
+    def append(
+        self,
+        df: pa.Table | pa.RecordBatchReader,
+        snapshot_properties: dict[str, str] = EMPTY_DICT,
+        branch: str | None = MAIN_BRANCH,
+    ) -> None:
         """
-        Shorthand API for appending a PyArrow table to the table.
+        Shorthand API for appending PyArrow data to the table.
+
+        Accepts either a ``pa.Table`` or a streaming ``pa.RecordBatchReader``.
+        See :meth:`Transaction.append` for streaming semantics and partition
+        limitations.
 
         Args:
-            df: The Arrow dataframe that will be appended to overwrite the table
+            df: An Arrow Table or a RecordBatchReader of records to append.
             snapshot_properties: Custom properties to be added to the snapshot summary
             branch: Branch Reference to run the append operation
         """
@@ -1401,14 +1473,18 @@ def dynamic_partition_overwrite(
 
     def overwrite(
         self,
-        df: pa.Table,
+        df: pa.Table | pa.RecordBatchReader,
         overwrite_filter: BooleanExpression | str = ALWAYS_TRUE,
         snapshot_properties: dict[str, str] = EMPTY_DICT,
         case_sensitive: bool = True,
         branch: str | None = MAIN_BRANCH,
     ) -> None:
         """
-        Shorthand for overwriting the table with a PyArrow table.
+        Shorthand for overwriting the table with a PyArrow Table or RecordBatchReader.
+
+        Accepts either a ``pa.Table`` or a streaming ``pa.RecordBatchReader``.
+        See :meth:`Transaction.overwrite` for streaming semantics and partition
+        limitations.
 
         An overwrite may produce zero or more snapshots based on the operation:
 
@@ -1417,7 +1493,7 @@ def overwrite(
             - APPEND: In case new data is being inserted into the table.
 
         Args:
-            df: The Arrow dataframe that will be used to overwrite the table
+            df: An Arrow Table or a RecordBatchReader of records to write.
             overwrite_filter: ALWAYS_TRUE when you overwrite all the data,
                               or a boolean expression in case of a partial overwrite
             snapshot_properties: Custom properties to be added to the snapshot summary