diff --git a/focus_converter_base/focus_converter/conversion_configs/gcp/0_dimension_dtypes_S001.yaml b/focus_converter_base/focus_converter/conversion_configs/gcp/0_dimension_dtypes_S001.yaml
new file mode 100644
index 00000000..f7d83c55
--- /dev/null
+++ b/focus_converter_base/focus_converter/conversion_configs/gcp/0_dimension_dtypes_S001.yaml
@@ -0,0 +1,8 @@
+plan_name: adds dtypes to the columns required for the conversion
+conversion_type: set_column_dtypes
+column: PlaceHolder
+focus_column: PlaceHolder
+conversion_args:
+    dtype_args:
+        -   column_name: billing_account_id
+            dtype: string
diff --git a/focus_converter_base/focus_converter/data_loaders/data_exporter.py b/focus_converter_base/focus_converter/data_loaders/data_exporter.py
index fa84f223..73fa3664 100644
--- a/focus_converter_base/focus_converter/data_loaders/data_exporter.py
+++ b/focus_converter_base/focus_converter/data_loaders/data_exporter.py
@@ -5,6 +5,7 @@
 
 import polars as pl
 import pyarrow.parquet as pq
+from pyarrow import Table
 
 
 def __writer_process__(
@@ -12,17 +13,17 @@ def __writer_process__(
 ):
     while True:
         try:
-            df = queue.get(timeout=0.1)
+            table = queue.get()
         except Empty:
             continue
 
-        if not isinstance(df, pl.DataFrame):
+        if not isinstance(table, Table):
             break
 
         pq.write_to_dataset(
             root_path=export_path,
             compression="snappy",
-            table=df.to_arrow(),
+            table=table,
             basename_template=basename_template,
         )
 
@@ -80,4 +81,4 @@ def collect(self, lf: pl.LazyFrame, collected_columns: List[str]):
 
         # compute final dataframe
         df: pl.DataFrame = lf.collect(streaming=True)
-        self.__queue__.put(df)
+        self.__queue__.put(df.to_arrow())
diff --git a/focus_converter_base/focus_converter/data_loaders/data_loader.py b/focus_converter_base/focus_converter/data_loaders/data_loader.py
index 4e7ac248..fc076d00 100644
--- a/focus_converter_base/focus_converter/data_loaders/data_loader.py
+++ b/focus_converter_base/focus_converter/data_loaders/data_loader.py
@@ -49,6 +49,11 @@ def load_pyarrow_dataset(self) -> Iterable[pl.LazyFrame]:
         with tqdm(total=total_rows) as pobj:
             for batch in scanner.to_batches():
                 df = pl.from_arrow(batch)
+
+                # skip if number of rows empty
+                if df.shape[0] == 0:
+                    continue
+
                 yield df.lazy()
                 pobj.update(df.shape[0])
 
diff --git a/focus_converter_base/pyproject.toml b/focus_converter_base/pyproject.toml
index c242eb79..128d6bd6 100644
--- a/focus_converter_base/pyproject.toml
+++ b/focus_converter_base/pyproject.toml
@@ -8,7 +8,7 @@ packages = [{ include = "focus_converter" }]
 
 [tool.poetry.dependencies]
 python = "^3.9"
-polars = "^0.20.6"
+polars = "0.20.10"
 pydantic = "^2.1.1"
 pandas = "^2.0.3"
 pyarrow = "^14"
diff --git a/focus_converter_base/tests/test_polars_pyarrow_compatibility.py b/focus_converter_base/tests/test_polars_pyarrow_compatibility.py
new file mode 100644
index 00000000..e3a3d55e
--- /dev/null
+++ b/focus_converter_base/tests/test_polars_pyarrow_compatibility.py
@@ -0,0 +1,50 @@
+import os
+
+import polars as pl
+import pyarrow as pa
+import pytest
+from unittest import TestCase
+import pyarrow.parquet as pq
+import pyarrow.dataset as ds
+import tempfile
+
+from focus_converter.data_loaders.data_loader import (
+    DEFAULT_BATCH_READ_SIZE,
+    FRAGMENT_READ_AHEAD,
+    BATCH_READ_AHEAD,
+)
+
+
+class TestPolarsPyarrowCompatibility(TestCase):
+    """
+    Test that the Polars and PyArrow data types are compatible.
+
+    When trying to load a PyArrow table into Polars, raises error AttributeError: 'pyarrow.lib.StructArray' object has no attribute 'num_chunks'
+    """
+
+    def test_polars_pyarrow_compatibility(self):
+        # Create a PyArrow table
+        table = pa.table(
+            {"a": [1, 2, 3], "b": [4, 5, 6], "c": [{"d": 7}, {"d": 8}, {"d": 9}]}
+        )
+
+        with tempfile.TemporaryDirectory() as tempdir:
+            pq.write_table(table, f"{tempdir}/test.pq")
+            table = pq.read_table(f"{tempdir}/test.pq")
+
+            pl.from_arrow(table)
+
+            os.system(f"ls -lh {tempdir}")
+            dataset = ds.dataset(tempdir)
+
+            # Load the PyArrow dataset into Polars, this will raise an error AttributeError: 'pyarrow.lib.StructArray' object has no attribute 'num_chunks'
+            scanner = dataset.scanner(
+                batch_size=DEFAULT_BATCH_READ_SIZE,
+                use_threads=True,
+                fragment_readahead=FRAGMENT_READ_AHEAD,
+                batch_readahead=BATCH_READ_AHEAD,
+            )
+
+            for batch in scanner.to_batches():
+                df = pl.from_arrow(batch)
+                self.assertIsInstance(df, pl.DataFrame)